Smart Healthcare Analytics: State of the Art (Intelligent Systems Reference Library, 213) 9811653038, 9789811653032

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Intelligent Systems Reference Library 213

Prasant Kumar Pattnaik Ashlesha Vaidya Suneeta Mohanty Satarupa Mohanty Ana Hol   Editors

Smart Healthcare Analytics: State of the Art

Intelligent Systems Reference Library Volume 213

Series Editors Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK

The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included. The list of topics spans all the areas of modern intelligent systems such as: Ambient intelligence, Computational intelligence, Social intelligence, Computational neuroscience, Artificial life, Virtual society, Cognitive systems, DNA and immunity-based systems, e-Learning and teaching, Human-centred computing and Machine ethics, Intelligent control, Intelligent data analysis, Knowledge-based paradigms, Knowledge management, Intelligent agents, Intelligent decision making, Intelligent network security, Interactive entertainment, Learning paradigms, Recommender systems, Robotics and Mechatronics including human-machine teaming, Self-organizing and adaptive systems, Soft computing including Neural systems, Fuzzy systems, Evolutionary computing and the Fusion of these paradigms, Perception and Vision, Web intelligence and Multimedia. Indexed by SCOPUS, DBLP, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at https://link.springer.com/bookseries/8578

Prasant Kumar Pattnaik · Ashlesha Vaidya · Suneeta Mohanty · Satarupa Mohanty · Ana Hol Editors

Smart Healthcare Analytics: State of the Art

Editors Prasant Kumar Pattnaik School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India Suneeta Mohanty School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India

Ashlesha Vaidya Royal Adelaide Hospital Adelaide, SA, Australia Satarupa Mohanty School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India

Ana Hol School of Computer, Data and Mathematical Sciences Western Sydney University Kingswood, NSW, Australia

ISSN 1868-4394 ISSN 1868-4408 (electronic) Intelligent Systems Reference Library ISBN 978-981-16-5303-2 ISBN 978-981-16-5304-9 (eBook) https://doi.org/10.1007/978-981-16-5304-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022, corrected publication 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

With an intention to bring forward the concept of Internet of Things (IoT) in health care and to innovate the new technology on the cutting edge, this book pools resources together with the technical knowledge from academics, scientists, and research scholars. It focuses on individual aspects of the IoT applications in the smart healthcare domain. This book facilitates the multidisciplinary thinking approaches that are required for the successful implementations of the IoT aspects in the healthcare domain. These include but are not limited to remote ongoing health monitoring, realtime health monitoring, diagnosis and analysis of disease patterns, applications of the underlying technologies, and the associated security requirements. The goal of this book includes the timeliness of healthcare activity, patient safety, and patient treatment with maximum efficiency and minimum expense. This book discusses the most recent trends, innovations, practical challenges, and concerns of IoT in the healthcare domain with the adopted solutions. The book is organized into 15 chapters. Chapter Smart Healthcare Analytics Using Internet of Things: An Overview introduces the IoT-enabled environment in the smart healthcare sector. It describes various aspects of IoT for smart healthcare analytics like background and benefit of IoT technology, its application in the health sector, different smart IoT devices used or available for health monitoring, and finally, it addresses the related security issues. Chapter Neuronal Reconstitution Effect of Stem Cell Transplantation for Smart Healthcare After Focal Cerebral Ischemia discusses the experimental study to obtain smart health care through histological and behavioural comparison analysis of brain injury by grafted cells into a brain tissue. The chapter closely examines the neuroregeneration and rehabilitation functions of rat model individuals through clinical application systems and suggests that stem cell therapy shows a powerful approach for the severe central nervous system. Stem cell therapy may be an excellent strategy for the treatment of severe stroke patients and potentially, stem cell transplantation may suggest an alternative cell-based treatment for brain injury. Chapter A Review of Cancer Detection and Prediction Based on Supervised and Unsupervised Learning Techniques has done one comparative analysis of learning-based methods on various research works on multi-dimensional problems

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of cancer disease. It discusses some of the new research directions in the development of deep learning approaches in cancer detection on Breasts, Lungs, Brain, and Prostate. It surveys various image-based cancer detection applications in recent years based on the image types, the application used, and performance measures. The uniqueness of the last 3 years’ studies is highlighted in the survey. Chapter Resource Management Challenges in IoT Based Healthcare System focuses on the resource management challenges by applying the cloud concept in the IoT environment. Resources are assigned to the healthcare device cloud on demand basis. Physical devices of IoT are shared and provisioned as in the cloud environment where resources are available to serve the request using Infrastructure as a Service (IaaS). This chapter discusses resource allocation and data management towards optimizing resource utilization. Chapter A Comprehensive and Analytical Study of Smart Healthcare with IoT explores the way smart health care can be developed with the use of IoT-based wireless technologies. Owing to the use of IoT devices in the healthcare industry has both benefits and drawbacks, this chapter delves into how each difficulty is met with a solution. Chapter Clinical Study on Itching Relief Caused by Dry Skin of Cosmetics Containing Ceramide NP and Guaiazulene in Smart Healthcare Products focuses on proving that the itching relief cosmetics containing 2% CER NP (ceramide NP ) and 0.02% GAZ (guaiazulene) have clinical effects on itching relief caused by dry skin by measuring skin moisture content. The research is conducted with the Bio & Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science & ICT (2017M3A9D8048416). Chapter Study and Impact Analysis of Machine Learning Approaches for Smart Healthcare in Predicting Mellitus Diabetes on Clinical Data proposes a machine learning method for diabetes prediction. The resultant system may also be useful for researchers to improve precise and operative tools which may help the medical professionals for taking good decisions about the disease position. Chapter A Review of Flying Robot Applications in Healthcare reviews different literature and discusses various applications of flying robot in the healthcare system and medical-related issues used worldwide. Additionally, this chapter has done one extensive study towards the representation of different areas where the flying robot can be used in the healthcare sector to modernize our medical sector using disruptive technology. Chapter Application of Deep Learning in Biomedical Informatics and Healthcare discusses the different deep learning techniques like medical image processing and its division, genomic sequences, gene pattern exploration, extrapolation in protein bonding, and its applications in the biomedical field. Apart from this, the chapter focuses on the most utilization of deep figuring out on how well-being informatics has included handling well-being information as an unstructured source. Chapter Integration of Machine Learning and IoT for Assisting Medical Experts in Brain Tumor Diagnosis compares different methods of feature extraction, segmentation method, and classification method using Machine Learning, and then deep

Preface

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learning is done separately in a tabular fashion. This comparison would result in giving accurate results and hassle-free treatment for the patient. Chapter The Relationship Between SNS Fatigue, SNS Immersion and Social Support Recognized by SNS Due to Smart Intelligence aims to find ways to mediate the lives of university students by investigating social support, SNS fatigue, and SNS immersion in order to maintain smart intelligence by identifying SNS usage. Several test cases have been made towards the study of the subject. Chapter Factors Affecting Attitudes Toward Death of Korean Nursing College Students; Including Spiritual Well-Being for Smart Healthcare proposes one narrative survey of 179 Korean nursing college students to investigate the degree of spiritual well-being and attitude towards death and to identify the factors affecting their attitude towards death. Chapter Development of Smart Healthcare Cosmetic Ingredients Using Lactic Acid Fermented Coffee presents one study that attempts to develop a new cosmetic ingredient by enhancing the physiological activity of coffee. It presents data on the possibility of a new smart healthcare cosmetic ingredient by the fermentation of lactic acid bacteria by comparing the antioxidant properties of cosmetic ingredients made from green beans fermented with lactic acid bacteria and confirming the organoleptic properties. Chapter Ranking of SARS-CoV-2 Vaccines with Reference to India ranks Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) vaccines using the Multi-Criteria Decision Making (MCDM) technique. Using the pairwise comparison matrix, it compares the priority, alternative, efficiency, price, storage, and variant of different vaccines. Chapter RFID Based Patient Billing Automation Using Internet of Things (IoT) aims to automate the billing process for patients in hospitals using RFID-based scanners for better transparency and hassle-free fast discharge of a patient. As a result, a bed can be provided to the other patient in need urgently in an emergency like the pandemic where procuring a bed instantly can save a life. This book in its present shape and value is possible due to the contribution of the very insightful papers of the authors, who have ensured that their papers are thoughtprovoking and will instigate many for such research. Starting from the Call for chapters till their finalization, all the editors have given their contributions amicably, which is a positive sign of significant teamwork. The editors are sincerely thankful to all the members of Springer especially Prof. Lakhmi C. Jain for providing constructive inputs and allowing an opportunity to edit this important book. We are equally

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thankful to the reviewers from across the globe for their support and for ensuring quality chapter submission. Lastly, we are sincerely thankful to the Almighty for supporting and standing with us at all times, whether it’s good or tough times, and given ways to concede us. Bhubaneswar, India Adelaide, Australia Bhubaneswar, India Bhubaneswar, India Kingswood, Australia

Prasant Kumar Pattnaik Ashlesha Vaidya Suneeta Mohanty Satarupa Mohanty Ana Hol

Contents

Smart Healthcare Analytics Using Internet of Things: An Overview . . . . Satarupa Mohanty, Suneeta Mohanty, Prasant Kumar Pattnaik, Ashlesha Vaidya, and Ana Hol

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Neuronal Reconstitution Effect of Stem Cell Transplantation for Smart Healthcare After Focal Cerebral Ischemia . . . . . . . . . . . . . . . . . . Tae Hoon Lee

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A Review of Cancer Detection and Prediction Based on Supervised and Unsupervised Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priya Mishra, Brijesh Raj Swain, and Aleena Swetapadma

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Resource Management Challenges in IoT Based Healthcare System . . . . Roshni Pradhan, Amiya Kumar Dash, and Biswajit Jena A Comprehensive and Analytical Study of Smart Healthcare with IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pratiksha Tripathy, Pradeep Kumar Mallick, and Debjit Koner Clinical Study on Itching Relief Caused by Dry Skin of Cosmetics Containing Ceramide NP and Guaiazulene in Smart Healthcare Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Su In Park, Jinseo Lee, and Moon Sam Shin Study and Impact Analysis of Machine Learning Approaches for Smart Healthcare in Predicting Mellitus Diabetes on Clinical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aisworya Mohanty, Sasmita Parida, Suvendu Chandan Nayak, Bibudhendu Pati, and Chhabi Rani Panigrahi

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A Review of Flying Robot Applications in Healthcare . . . . . . . . . . . . . . . . . 103 Ritu Maity, Ruby Mishra, and Prasant Kumar Pattnaik

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Application of Deep Learning in Biomedical Informatics and Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Deepak Kumar Nayak, Pragatika Mishra, Puja Das, Asik Rahaman Jamader, and Biswaranjan Acharya Integration of Machine Learning and IoT for Assisting Medical Experts in Brain Tumor Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Aastha, Sushruta Mishra, and Satarupa Mohanty The Relationship Between SNS Fatigue, SNS Immersion and Social Support Recognized by SNS Due to Smart Intelligence . . . . . . . . . . . . . . . . 165 Shinhong Min and Soon-Young Yun Factors Affecting Attitudes Toward Death of Korean Nursing College Students; Including Spiritual Well-Being for Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Weon-Hee Moon and Soon-Young Yun Development of Smart Healthcare Cosmetic Ingredients Using Lactic Acid Fermented Coffee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Hye-jin Kwon Ranking of SARS-CoV-2 Vaccines with Reference to India . . . . . . . . . . . . 199 Proshikshya Mukherjee, Sudhir Kumar Rath, Sibanand Mishra, and Prasant Kumar Pattnaik RFID Based Patient Billing Automation Using Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Suneeta Mohanty, Pranav Shekhar, Siddhartha Sinha, Arnab Poddar, Gevendra Sahu, and Ayushi Dash Correction to: Resource Management Challenges in IoT Based Healthcare System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roshni Pradhan, Amiya Kumar Dash, and Biswajit Jena

C1

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

Editors and Contributors

About the Editors Dr. Prasant Kumar Pattnaik, Ph.D. (Computer Science), Fellow IETE, Senior Member IEEE is a Professor at the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. He has more than a decade of teaching and research experience. Dr. Pattnaik has published numbers of Research Papers in peerreviewed International Journals and Conferences. He also published many edited book volumes in Springer and IGI Global Publication. His areas of interest include Mobile Computing, Cloud Computing, Cyber Security, Intelligent Systems and Brain Computer Interface. He is one of the Associate Editor of Journal of Intelligent & Fuzzy Systems, IOS Press and Intelligent Systems Book Series Editor of CRC Press, Taylor Francis Group. Dr. Ashlesha Vaidya, MBBS, FRACP, FAANMS is a Staff Specialist Geriatrician at the Royal Adelaide Hospital. She has a wide range of experience in Geriatric Medicine, having worked in hospital and community settings in the public sector over the last decade. She co-edited 4 books for Springer and World Scientific few years ago. Dr. Suneeta Mohanty, Ph.D. (Computer Science), is working as Assistant Professor at the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. Dr. Mohanty has published several research papers in peerreviewed International journals and conferences including IEEE and Springer as well as served as organizing chair (SCI-2018). She was appointed in many conferences as Session chair, reviewer, and track co-chair. Her research area includes Cloud Computing, Big Data, Internet of Things, and Data Analytics. Dr. Satarupa Mohanty, Ph.D. (Computer Science), is working as Associate Professor at the School of Computer Engineering, KIIT Deemed to be University,

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Bhubaneswar, India. Dr. Mohanty has published several research papers in peerreviewed International journals and conferences. She was appointed in many conferences as Session chair, reviewer, and track co-chair. Her research area includes Bio Informatics, Big Data and Internet of Things. Dr. Ana Hol is an Associate Professor and Associate Dean, Learning and Teaching, School of Computer, Data and Mathematical Sciences at the Western Sydney University. She graduated from the University of Western Sydney with B.A., first class honours and for her achievements was awarded the university medal. Her research interests are in the areas of Information Systems, SMEs (Small and Medium Enterprises) Information Technology use, acceptance and adoption; eTransformation and eCollaboration of the businesses within developed and developing countries; Information Systems and applications for education; social networking technologies; process optimization and knowledge management. Ana is a member of Enhanced Living with Information Systems Research Group and an associate member of Tele health Research and Innovation Laboratory. As a part of Ana’s Ph.D. research study, she developed eTransformation guide methodology for the SMEs (eT Guide). She wrote a book and a number of referred conference papers. Ana organised three international conferences. She is a co-editor of the International Journal on Advances in ICT for Emerging Regions and a program committee member and the reviewer for 14 international conferences. Her teaching interests are in the areas of eTransformation, eBusiness, Enterprise Information Management and Information Systems Deployment and Management. Ana is a winner of the Australian Computer Society (ACS) Disruptor ICT Educator of the Year (2018) Award and the South East Asia Regional Computer Confederation Global ICT Educator of the Year 2018 Award.

Contributors Aastha School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India Biswaranjan Acharya School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India Puja Das Hiralal Majumdar Memorial College for Women, Kolkota, India Amiya Kumar Dash KIIT Deemed to be University, Bhubaneswar, India Ayushi Dash School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Ana Hol School of Computer, Data and Mathematical Sciences, Student Centre, Western Sydney University, Penrith, Australia Asik Rahaman Jamader Penguin School of Hotel Management, Kolkota, India

Editors and Contributors

Biswajit Jena International Bhubaneswar, India

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Technology

(IIIT),

Debjit Koner School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Hye-jin Kwon Department of Chemical Engineering, Soongsil University, Dongjak-gu, Seoul, Korea Jinseo Lee Department of Senior Healthcare Majoring in Cosmetic Formulation & Pharmacology, Eulji University, Seongnam, Korea Tae Hoon Lee Emergency Medical Technology Department, Namseoul University, Cheonan, Korea Ritu Maity Research Scholar, School of Mechanical Engineering, KIIT, Bhubaneswar, India; Senior Engineer, Central Tool Room, and Training Center, Bhubaneswar, Government of India, Bhubaneswar, India Pradeep Kumar Mallick School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Shinhong Min Department of Nursing Science, Baekseok University, Dongnamgu, Cheonan-si, Chungcheongnam-do, Republic of Korea Pragatika Mishra Bhubaneswar, India Priya Mishra School of Computer Engineering, KIIT University, Bhubaneswar, India Ruby Mishra Associate Professor, School of Mechanical Engineering, KIIT, Bhubaneswar, India Sibanand Mishra KIIT Deemed to be University, Bhubaneswar, Odisha, India Sushruta Mishra School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India Aisworya Mohanty Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India Satarupa Mohanty School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Suneeta Mohanty School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Weon-Hee Moon Department of Nursing Science, Pai Chai University, Daejeon, Republic of Korea Proshikshya Mukherjee KIIT Deemed to be University, Bhubaneswar, Odisha, India

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Deepak Kumar Nayak Vikash School of Business Management, Bargarh, Odisha, India Suvendu Chandan Nayak Department of Computer Science and Engineering, GITA Autonomous College, BPUT, Bhubaneswar, India Chhabi Rani Panigrahi Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India Sasmita Parida Department of Computer Science and Engineering, GITA Autonomous College, BPUT, Bhubaneswar, India Su In Park Department of Senior Healthcare Majoring in Cosmetic Formulation & Pharmacology, Eulji University, Seongnam, Korea Bibudhendu Pati Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India Prasant Kumar Pattnaik School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Arnab Poddar School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Roshni Pradhan KIIT Deemed to be University, Bhubaneswar, India Sudhir Kumar Rath KIIT Deemed to be University, Bhubaneswar, Odisha, India Gevendra Sahu School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Pranav Shekhar School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Moon Sam Shin Department of Senior Healthcare Majoring in Cosmetic Formulation & Pharmacology, Eulji University, Seongnam, Korea Siddhartha Sinha School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Brijesh Raj Swain Department of Medicine, Siksha O Anusandhan University, Bhubaneswar, India Aleena Swetapadma School of Computer Engineering, KIIT University, Bhubaneswar, India Pratiksha Tripathy School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India Ashlesha Vaidya Royal Adelaide Hospital, Adelaide, Australia Soon-Young Yun Department of Nursing Science, Baekseok University, Dongnamgu, Cheonan-si, Chungcheongnam-do, Republic of Korea

Smart Healthcare Analytics Using Internet of Things: An Overview Satarupa Mohanty, Suneeta Mohanty, Prasant Kumar Pattnaik, Ashlesha Vaidya, and Ana Hol

Abstract Smart Healthcare Systems contained three domains: communication and networking, body area, and service. It is capable of improving healthcare quality by remote monitoring, reducing the cost and efficient functioning of various medical devices. Integration of IoT with Big data and cloud computing can solve several real-time problems smart health care application. In these applications, the cloud computing provides a common workplace for IoT and big data, big data provides the data analytics technology and IoT provides the source of data. This chapter gives an overview including benefits, application and challenges of smart healthcare analytics in IoT enabled environment. Keywords Internet of Things (IoT) · Healthcare · Wireless Body Area Networks (WBANs) · Security

S. Mohanty · S. Mohanty (B) · P. K. Pattnaik School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India e-mail: [email protected] S. Mohanty e-mail: [email protected] P. K. Pattnaik e-mail: [email protected] A. Vaidya Royal Adelaide Hospital, Adelaide, Australia A. Hol School of Computer, Data and Mathematical Sciences, Student Centre, Western Sydney University, Penrith, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_1

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1 Introduction Owing to the advancement in recent technology, its growing expense in the healthcare industry and consumer electronics the vision of e-health system has extends to the Personal Health Device (PHD). The fusion of the healthcare industry with Information and Communication Technology (ICT) evolved the possibility of optimizing and providing reliable medical resources, competent services to patients, aged people with chronic illness, and physically disabled patients. The contemporary food habit and sedentary lifestyle commence chronic diseases like hypertension, Cardio Vascular Disease (CVD), stroke, blood pressure, and diabetes. The researcher of the World Health Organization (WHO) states that Cardio Vascular disease realizes 31% of global death, the growth of diabetic patients and other chronic disease patients increases in an exponential order every year. The growing expense of healthcare service pressurizes in furnishing efficient and effective healthcare facilities in a maximum of the developing countries. The emerging technology: Internet of Thing (IoT) interconnect things and humans anytime, anywhere. IoT also extends its scope to interact and exchange the associated data with its users. By the end of the year 2020, 50 billion things or devices are expected to be linked with the Internet through IoT. For the betterment of medical facilities, IoT merges information technology and telecommunication. By the reason of this, the medical-related information can be easily passed on from one location to the other and the diagnosis of disease with the arrangement of its related facilities can be made to improve the patients’ condition. Owing to the technological facility, the healthcare service now can be available remotely over long distance and the cost of the service can also be minimized. Along with these, for the patient with chronic diseases, IoT facilitates less hospital stay less movement, and provision of shared professional and clinical guidance.

2 Internet of Things In recent decades, the revolution on the internet exerts its influence in all kinds of business through the introduction of new technology in every aspect [1]. The possibilities of ubiquitous communication are only happening due to the help of these new technologies like wireless network connection and communication, availability of all kind of sensors, etc. Owing to the potentiality of the IoT in regards to earning a valuable and great income, different business owners introduced IoT as an innovative solution to the ICT world. Internet of Technology incorporates the requirements of the current era through a single system that interconnects networks with person, device, place, and time. In the domain of healthcare monitoring techniques, IoT plays the character of building blocks. An efficient IoT-based healthcare monitoring system aims to furnish remote

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monitoring of patient conditions in real-time to prevent patients to enter the condemnatory health condition. In addition to this, it also upgrades the lifestyle of patients through smart IoT surroundings. For security, privacy, and working of the IoT system new challenges are always introduced on a day-to-day basis. Owing to the global accessibility and connectivity the information security in IoT is a bit difficult and complicated. Hence, privacy and security is the vital part in the design of any IoT deployment or project. In 1999, IoT was first proposed by Kevin Ashton [2]. Accordingly to his inventions, IoT is distinctly identifiable and interoperable connected devices with the inbuilt radio frequency of technology with the communication are the main driving factor of IoT. Wireless and wired networks of sensors and actuators, identification and tracking automation, advanced communication protocol with distributed technology for smart things are most pertinent [3]. The IoT fundamentals as a combination of emerging technology and internet is cited in Korteum’s [4] article. IoT and the healthcare domain can be interlinked to enhance the quality of living style and can promote to assist the basic need through mobile or digital application. In health monitoring equipment, the sensor can be integrated from which the information can be collected and made available to the doctor via app or internet in order to adequate responsiveness and give proper timely treatment. Furthermore, IoT sensors and actuators can keep track of ongoing medicine or new medicine with their risk factors and can evaluate adverse effects and reactions for any allergic case. Due to the sensing technology of IoT, one can keep track of heartbeat rate, body temperature, blood pressure, oxygen level, etc. In emergency cases of patient family members, doctors can be notified of the status of the patient with relevant data. This method is more convenient and helpful for disabled people and senior citizens who live alone.

2.1 Benefits of IoT On the ground of various domains, Muralidharan describes the potentiality of IoT [5] are as follows. a. b.

c.

d.

Sensing Location: Location tracking can be made possible by the use of RFID. Monitoring Traffic: For the infrastructure of the smart city, IoT effectively provides traffic management and control by the use of technology, networks, and smart devices. Monitoring Environment: IoT opens the door for the smart environment by facilitating disaster forecast, pollution control. In case of any emergency, it triggers the alarm for incorporating appropriate measures. Monitoring Remote e-health: Through IoT device or App patient’s information can be collected on a real-time basis and can be remotely monitored and controlled.

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Monitoring Remotely: For controlling the appliances of the machine remote monitoring can help for energy conservation, detection of emergency and antitheft issues. Secure Communication: For the provision of the right privacy features and security of personal information IoT architecture is designed and employed. Ad-hoc network: It facilitates the reorganization of the internet network to form prevalent connectivity.

3 IoT and Smart Healthcare Smart healthcare systems contained three domains: communication and networking, body area, and service. The domain body area is characterized by numerous Wireless Body Area Networks (WBANs) which have a one-to-one correspondence with the user. The domain communication and networking take care of making the bridge between the body area domain and the service domain. As the name suggested, WBAN is a wireless network that consists of several sensors interconnected through wearable smart devices with some computing capability. WBAN smart devices can be located outside or inside human body parts and connected to the internet through the gateway devices. Due to the network connectivity, in online mode, the patient’s relevant data can be collected even the presence of the patient is in a remote area. The application layer protocol MQTT facilitates the message transmission service by connecting all smart devices in a constraint environment. Compared to other protocol, MQTT is a lightweight protocol which is based on publish-subscribe model. Smart devices usually consume fewer memory resources, can operate with low bandwidth, and carry limited processes. The dynamic representation of MQTT data uses a pattern matching algorithm to compare and analyze the existing data with the collected data to take the next appropriate decision. In case of any diagnosis of expected disease, the appropriate care is realized by the caretaker in the emergency situation. The collected health data is forwarded to the assigned doctor for necessary treatment. In the domain of e-healthcare services and telecommunication services [6], IoT enhances market potential. IoT intensifies the business intelligence in the health sector and provides a comfortable service in the hospital [7]. Due to the ability to monitor the ongoing activity of prone patients or ordinary patients. IoT can prevent the possibility of predominant disease to happen and thus can conduct appropriate medical attention [6, 8]. This novel innovation can empower the seek persons and does the enhancement in the health sectors’ business profit. To some extent, it supports and builds on peoples’ concern about health, exerts a positive influence on the patients’ social issues, upgrades the quality of their lifestyle [9]. Additionally, due to the inclusion of modern technology in the IoT environment the economic prosperity of the health domain has been enriched [10]. The production of hospital-related material and elimination of hospital waste generally impacts the environment. However, due

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to the use of IoT the production and garbage elimination can be managed in a better way and hardly damage the environment [11]. In the demand for medical attention, the adoption of IoT provides innovative and smart services to people and patients. Additionally, it fixes up the security, facilitates easy access to suitable medical attention in the emergency situation, takes care for the improvement of health condition, looks after for providing continual care and instantaneous support for the chronic disease. Altogether, the IoT improves the quality of life to a greater extent and exceptionally upgrades the health sector [6].

4 IoT Application in Health Sector A comprehensive study on classification on the application area of IoT in the smart health sector domain is presented by (IERC). From the application areas, some kinds are types of services and some are types of devices or products. The application areas in the smart health domain are distributed as follows. i. ii.

iii.

iv.

v.

vi.

vii.

viii.

Fall Detection: For the elderly and physically challenged people IoT usage facilitates living independently. Medical Fridge: Some sensitive organic components should be kept with some extensive care like internal protective temperature control. IoT technology provides a suitable environment for this with the feature of object interaction. Sportsperson Care: For professional athletes, IoT smart devices provide applications to measure different parameters like daily extensive level, weight value, sleep duration, blood pressure, and so many other factors. Patient Surveillance: For monitoring and caring the elderly persons and the patients who stay at home, IoT creates remote in-hospital ambiance and provides the relevant update to the doctor in an online mode. Management of Chronic Disease: The use of smart medical IoT devices can replace the physical medical attendant, reduces the duration of being in a hospital, reduces traffic and fuel consumption, and ultimately results to minimize the expense of the patient. Hygienic Hand Control: Pollution of environment can be identified and can be controlled by linking smart devices like RFID designing for measurement of emissions. Sleep Control: At the time of sleep of patients, smart IoT devices can be interconnected to identify the status of the patient like blood pressure, heart rate, pulse and can be used for further analysis. Dental Health: Smart toothbrushes having Bluetooth-enabled facilities can record persons’ brushing information through the help of smartphone apps to study someone’s dental status and can share the details with the dentist.

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5 IoT Device for Monitoring Healthcare In view of the fact that IoT has plunged into the healthcare sector, the upcoming decade will take the benefit of availing upgradation in the appropriate diagnosis of the disease and its genuine treatment. Due to the essence of adopting new technology quickly, IoT continues to innovate novel things in the health sector using the extensive web-connected universe. For the instance, through the virtual visit and remote monitoring of IoT smart devices, a good bonding can be established between patient and doctors, the care of patients with chronic diseases can be realized through IoT smart devices, automation of heart patients’ care workflow can be reached, speedy collection and analysis of medical information can be made possible with better efficiency and no error. In the manufacturing process of pharmaceutical materials, IoT technology boosts up the process which in turn minimizes the drug prices and streamline the healthcare cost as a whole. Some of the IoT devices with their applications in the healthcare domain are discussed as follows.

5.1 Monitoring Patient Remotely Collaboration of modern medical facility with IoT is capable of remotely monitoring patients. The availability of so many wearable devices with associated sensors or detectors can able to watch the status of the patient and can inform the doctor. In the rural area where the reachability of doctors is infrequent, this device helps the unprivileged people to avail of doctor’s guidance and thus reduces the rate of death in the rural area. Owing to the potential of these IoT wearable devices, its demand in the healthcare sector increases, and easy access to this application made possible anywhere anytime.

5.2 Glucose Monitoring The chronic disease diabetes results in some abnormal activity of the pancreas gland and disturbs the production of insulin hormone. In the deficiency and absence of insulin in the blood, the fluctuation in sugar takes place which internally damages some vital organs. IoT application in the wearable device is composed of embedded body sensors which have the capability of continuously monitoring sugar level and sending data to the doctor. This reduces the risk of death factor due to diabetes.

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5.3 Connected Inhaler Asthma is a chronic disease that can be controlled by the usage of the inhaler, but not curable. This asthma attack is symptomatic or can be noticed within few hours by the patient. The role of IoT in healthcare applications is vital. The sensor attached to the inhaler can measure the temperature or air pollution factor and can trigger an alarming indication to the patient to prevent the asthma attack.

5.4 Connected Contact Lenses The eye is one of the most important organs. Tears create one unseen thin layer over the eye to provide protection and to keep it functioning properly. With the combination of contact lenses and sensors, the smart IoT device can detect the symptoms of numerous diseases from tear samples which can be considered as one uncommon achievement of IoT in healthcare.

5.5 Hearing Aid The problem of loss of hearing is now a common component. People are now well familiar with IoT-based hearing support.

5.6 Testing Blood Coagulation In the human body, blood coagulation levels take a vital role. Towards the adequate treatment of several diseases like stroke, diabetes it is important to examine the blood coagulation or clotting level. Smart IoT devices can monitor and test the blood-clotting level now and again, and keep track of the patient’s activity which plays the main support for the appropriate treatment and ultimately lower the risk factor.

5.7 Smart Watches IoT with artificial intelligence and machine learning provides innovative healthcare tools. These wearable smart watches can able to check the irregularities in breathing, measures the ECG value by the usage of an electrical heart sensor, and can check other heart-related problems. Additionally, these smart watches can monitor blood

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pressure, can be used as a sleep tracker, activity tracker, and movement reminder, predict the depression level, etc.

5.8 Cancer Detection IoT has proofed its potential and has been witnessed to provide an efficient and accurate diagnosis of breast cancer which ultimately helps a lot to provide appropriate treatment in the early stage. The IoT smart device “ITbra” plays the main role in effectively detecting breast cancer compared to the usual cancer detection process. It consists of seven embedded tissues which record the changes in temperature in a regular interval.

5.9 Efficient Drug Management Managing the drug’s production to its distribution and usage by needy patients is a challenging job. IoT application in healthcare introduces a revolutionary solution. For the efficient monitoring and uniform distribution process, the IoT smart tag provides a reasonable solution.

5.10 Body Scanning In this busy life, physical fitness is now the main focus for everyone. However, the regular body checkup is a tedious and costly task. In IoT healthcare, the smart body scanner (that includes a full-length mirror with 3D cameras, a mobile application, and a weight scale) is a blessing. This application keeps track of any change in the body part or organ including each required parameter and gives a competitive outcome.

5.11 Monitoring Heart Rate In the world, the figure of cardiac disease or heart patients is growing slowly but surely. Smart IoT device with the mobile application is capable of recording pulse rate heartbeat. If the recorded value, in any case, deviates from normal behaviour then the sensor connected to the IoT smart devices sends the real-time pulse rate and heartbeat rate to the cloud and doctors can check the data from anywhere on a real-time basis. This eventually reduces the fear of a late response to the attack and thus reduces the risk factor. This application provides real-time supervision and adequate health management which is necessary for heart patients to a greater extent.

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From various smart devices for heart patients, “Asus Vivo Watch” is the best one that overcomes the uncomfortable chest strap problem by simple wrist wearable device. It safely measures faultless heartbeat rate with a smart built-in optical sensor and Vivo pulse technology. It collects the concerning health insights from one’s heartbeat like how many calories have burnt and what is the appropriate measure of exercise one has to carry out as a result of this.

5.12 Quell Relief Now a days, the restful sleep, relaxation, relief from anxiety and tension is a dream. However, in the medical and health care field Quell Relief is one wearable smart device with IoT technology that brings the dream into existence. This device is capable of supplying the level of stimulation required by a person in the daytime and balances the appropriate level of stimuli at nighttime by automatically switching to night therapy when it is not required. Due to this, a person can enjoy the peace of restful sleep.

5.13 Hospital Waste Management Hospital waste can be treated as a gateway to contamination and mass infection which is required to be deal with smart and cautious way. Sensors embedded in the dustbin can be treated as automated medical waste management. These smart dustbins can be positioned in different sections or parts of the hospital and can record the quantity of collected waste. When the trash exceeds its threshold value, the garbage collection can happen through an automated robot and then can be dumped outside of the hospital.

6 Security Issues Smart Healthcare System is capable of improving healthcare quality by remote monitoring, reducing the cost and efficient functioning of various medical devices. To provide secure and trust worthy architecture for Smart Healthcare System is still a challenge. In a healthcare system, required data are to be collected from patients and sent through secure communication channel. Secure smart healthcare system represents Client–Server architecture. Here, data acquisition is done by the Clients/Patients and these data are stored in the Server which is located in cloud. Since the sensor nodes of the client system are having limited computing power and storage capacity, the client send the data through secure channel to the server. The architecture of Smart Healthcare System is subject to various cyber attack in every level starting from

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physical device, communication channel to cloud data storage. The main objective of secured IoT system is to provide privacy, integrity, authentication and availability. Some of the key privacy issues are listed below [12]: Exposure of patient’s personal data: The main objective of the secured system is not to reveal patient’s personal data [13]. Otherwise these confidential patient data may attract cyber attack to benefit others. Data Snooping: As the IoT based healthcare system uses wireless network for the data transmission, it increases the chance of data snooping which will lead to many problems in future. So protection of data should be taken care while transmission [14]. Patient’s location privacy: It should be taken care while using smart healthcare system by hiding the patient’s location message [15]. Secured Smart Healthcare System can be achieved with the joint effort of IoT device manufacturer, Cloud service provider and healthcare organization. IoT device manufacturer should issue a digital certificate for each device to ensure authentication. Healthcare information should be transmitted in encrypted form. Digitally signed certificate ensures the integrity of the transmitted data.

7 Conclusion Advances in Internet technologies have lead to increase the prospects of Smart Health Care System. For Patient point of view, timely, personalized and appropriate medical services can be availed using Smart Health Care System. For health organizations point of view, smart health care results in reduction of cost, individual employee pressure, better management of information and efficient patient care. However, new technologies should focus on security aspect of the system to achieve privacy, integrity, authentication and availability.

References 1. Premkumar, G., Roberts, M.: Adoption of new information technologies in rural small businesses. Omega 27(4), 467–484 (1999) 2. Shancang, L., Li, D.X., Shanshan, Z.: The internet of things: a survey. Inf. Syst. Front. 17(2), 243–259 (2015) 3. Atzori, L., Lera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010) 4. Korteum, G., Kawsar, F., Fitton, D., Sundramoorthy, V.: Smart objects as building blocks for the internet of things. IEEE Internet Comput. 1(51), 44–51 (2010) 5. Muralidharan, S., Roy, A., Saxena, N.: An exhaustive review on internet of things from Korea’s perspective. Wirel. Pers. Commun. 90(3), 1463–1486 (2016) 6. Vermesan, O., Friess, P.: Internet of Things-From Research and Innovation to Market Deployment. River Publishers (2014) 7. Roman, R., Najera, P., Lopez, J.: Securing the internet of things. Computer 44(9), 51–58 (2011)

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8. Bandyopadhyay, D., Sen, J.: Internet of things: applications and challenges in technology and standardization. Wirel. Pers. Commun. 58(1), 49–69 (2011) 9. Helal, A., Cook, D.J., Schmalz, M.: Smart home-based health platform for behavioral monitoring and alteration of diabetes patients. J. Diabetes Sci. Technol. 3(1), 141–148 (2009) 10. Haller, S., Karnouskos, S., Schroth, C.: The Internet of Things in an Enterprise Context, pp. 14– 28. Springer, Berlin (2009) 11. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. Commun. Surv. Tutor. (IEEE) 16(1), 414–454 (2014) 12. Ziegeldorf, J.H., Morchon, O.G., Wehrle, K.: Privacy in the internet of things: threats and challenges. Secur. Commun. Netw. 7(12), 2728–2742 (2014) 13. Bruce, N., Sain, M., Lee, H.J.: A support middleware solution for e-healthcare system security. In: 16th International Conference on Advanced Communication Technology (2014). https:// doi.org/10.1109/ICACT.2014.6778919 14. Li, C., Raghunathan, A., Jha, N.K.: Hijacking an insulin pump: security attacks and defenses for a diabetes therapy system. In: 13th IEEE International Conference on e-Health Networking Applications and Services, pp. 150–156 (2011) 15. Ren, Y., Chen, Y., Chuahy, M.C.: Social closeness based clone attack detection for mobile healthcare system. In: IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012), pp. 191–199 (2012)

Neuronal Reconstitution Effect of Stem Cell Transplantation for Smart Healthcare After Focal Cerebral Ischemia Tae Hoon Lee

Abstract This study shows to obtain smart healthcare through histological and behavioral comparison analysis of brain injury by grafted cells into brain tissue. We used MCAO rat to experimentally investigate the extent of stroke-induced cerebral infarction and smart intelligence with IoT. The experiment was proceeded. Experimental rats (n = 36) were separately assigned to two groups after ischemic surgery: We divided into two different groups in testing the sham-control of PBS-only group and mESC injection group. Significant functional benefit in the experimental stage resulted by the transplantation procedure of embryonic stem cells into damaged brain tissue. We found behavioral and immunological differences between two groups. We study to closely examine the neuro-regeneration and rehabilitation functions of rat model individuals through clinical application systems. This research suggests that cell therapy shows a powerful approach for severe central nervous system. Keywords Smart healthcare · MCAO · Smart intelligence · Stroke · Neuro-regeneration

1 Introduction This study uses embryonic stem cell lines being used grafted cells to clinically treat stroke in ischemic condition to observe the effects of treatment [1]. The experimental rat of cerebral ischemia models histologically dyed with TCC method and found the damage using immunological and behavioral sensations and motor neuronal analysis [2]. The main reason for cell death by ischemia-low oxygen damage in the brain is ischemia in the blood vessels around the mid-cerebral artery [3]. Previous studies have been researched and their clinical importance has been highlighted in relation to the apoptosis of brain neurons due to ischemia [4]. Since the primary purpose of early stroke treatment is to minimize the patient’s damage, it is critical to protect the brain injury by correcting breathing and preventing complications. These measures include T. H. Lee (B) Emergency Medical Technology Department, Namseoul University, Cheonan 31020, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_2

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respiratory management, circulatory system management, treatment of abnormalities in body temperature control, treatment for urination and bowel movements, and treatment for brain edema [5]. Cerebral ischemia is described as a state of reduced blood stream so that tissue cannot normally maintain its function. The neuro-cellular damage mechanism after cerebral ischemia can be divided into two periods: It has been reported that abnormalities in energy due to oxygen and glucose depletion in bloodstream tissue. Previous studies showed that protein denaturation and glutamate accumulation in nerve cells eventually could make a cell necrosis. Post-neurocellular damage is basal nuclei of transient ischemia, that is known to involve glutamate toxicity [6]. Tissue death clinically could be treated to application of stem cell therapy. We now report that mES cells could be related to undergo morphologic and immunologic results consistent with neuronal differentiation. Embryonic stem cell treatment may use a representative technique for neuronal regeneration, potentially applying to the alternative treatment of various neurological diseases.

2 Method and Materials 2.1 Experimental Rat Surgery The animals were targeted at Sprague–Dawley-based white rat weighing 200–240 g (Korea Animal Center), 10 sham animals with PBS treatment after transient cerebral artery occlusion, and 26 experimental animals with transient cerebral artery occlusion and implantation. To prevent hypothermia caused by anesthesia, maintain the body temperature at 37 °C using an automatically controlled heating plate. On the first day of the MCAO procedure, the brain-damaged rat model was anesthetized by mixing 5% enflurane anesthesia with oxygen respirator, and then used an operating table for intra-brain transplantation.

2.2 Middle Cerebral Artery Occlusion Surgery Transient damage followed by reperfusion has shown to bind the right mid-brain artery. First anesthetize the experimental animal using 5% enflurane anesthesia, then cut the medial line of ECA and inner carotid artery (ICA) located outside the two sides. We cut by 1 cm at the base of the common carotid artery (CCA). The 6–0 silk suture loosened the ECA’s blocked area, temporarily positioned the microvascular clip on the ECA stem. We used micro-gift to puncture the ECA’s blocked area, and pushed a strand of 20 mm 3–0 nylon thread into the ECA’s diameter. The resistance is felt, indicating that the top of the thread has stopped the ICA basin within two rivers and the MCA’s visible site. This surgery method can block the blood flow from the ICA to the beginning of artery. After drilling at a position 3 mm laterally in

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Bregma, we slowly insert needles to a depth of 3.5 mm by a 10 μl Hamilton microinjector. After transplantation, we continue the study with the purpose of neurological restoration so that nerve cell regeneration and synaptic connections can be re-formed in damaged tissues by transplanting embryonic stem cell lines with needles for about 5 min before removing the Hamilton microinjection.

2.3 Motor and Sensory Behavioral Index Rats were received PBS showed a little behavioral improvement 28 days after transplantation while experimental group of mESCs transplantation expressed significant improvement on NSS index and performed functional benefit better than the shamcontrol group (Fig. 4). Sham group and cell-grafted group also were evaluated the difference in a degree of sensorimotor activities. To evaluate the motor function more accurately, all animals were pre-practiced on the treadmill 1 week prior to ischemic induction.

2.4 Histological Characteristics of Infarction Lesion 28 days after brain ischemia, the experimental animal was killed and the brain was extracted, and the slices was made a brain incision of 2 mm thick. The slices were produced in the internal distance of the rat brain to create these fragments. After the cutting, the brain incision was found in a formalin solution that was refrigerated at 4 °C. Normal brain tissue typically stains with TTC was shown in red color, but infarcted lesions was shown the reduced red staining. TTC staining after 4 weeks applied to cell treated group and control group which was shown in the bleached staining in the corpus striatum and cortex. Embryonic stem cell treatment would contribute to reduce infarction size. Brain sections were shown that rats were injected to proceed transplantation with PBS (A; n = 10) or mESC (B; n = 26). The difference of infarct brain volumes from control group and cell-treated group are shown in the graph (Fig. 1).

2.5 Differentiation of Mouse Embryonic Stem Cell Immunohistochemical staining is used with Nestin and GFAP. Two different neuronal lineage markers were imaged by immunofluorescence microscopy. The mES cells could migrated from the injection site to the infarction region. However, the number of survived cells has limited to migrate from the injection to the infarction site. Majority of the grafted cells were moved to the center of the damaged lesion and were differentiated into various neuronal cells. The endogeneous cellular environment

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Fig. 1 Brain slices are shown with TTC-staining after 28 days: it is indicated that red region is viable tissue and white is ischemic area. a PBS, b mESC

influenced their differentiation of neuron and glial cell and the implantation cells were shown neuron cell phenotype and astrocyte.

2.6 Analysis of Results Values are presented as mean ± SD; Significant difference was analyzed from respective vehicle treated animals (P < 0.01). The statistics of the experimental index are handled by the Sigma Plot.

3 Results and Discussion The researchers’ prior study of stem cell-related experiments showed that the rat model had cerebral ischemia to see the effect of transplantation on brain ischemia, and then TTC was dyed, but not much of neocortex was found on the ischemic east side. The results showed that the middle cerebral artery was found to have been injured in almost all areas of blood supply in the control group, while the experimental group showed that the injured area was reduced. The above results show that embryonic stem cells could protect the nerve cells by reducing the volume of cerebral infarction. Until now, various stem cell experiments by researchers have derived motor and sensory functional improvements through regeneration of nerve cells, and future experiments suggest that more accurate histological analysis and behavioral improvement data will be obtained. TTC staining of ischemic brain with MCAO for 1 h dyed after reperfusion. TTC dye is performed to check the normal cell tissue area and the degree of injury caused by ischemia. In the control rats, the area where the nerve cells are indicated to have died out due to the TTC appears to be all or part of the blood-supplied striatum, which appears in many parts of the neocortex in the cerebrum, and is severely bleached and unable to find red light. The core bleaching area which appears was shallowly shown by its damaged area. The result is indicating injury in parts of the neocortex and

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striatum and the degree of bleaching was not severe compared to the control group (Fig. 1). The central bleaching is limited to the ischemic-induced region, and the bleaching area is also limited with the white color region. Grafted cell would be differentiated and some neuronal cells which the Nestine marker was shown (Fig. 2). We also could find that glial cells would show GFAPpositive cells expressed the GFAP of immunological cell marker after 4 weeks in Fig. 3. Grafted cell lines can be recognized in the specific infarction region with the specific maker. Each group were evaluated the functional enhancement of sensorimotor activities after implantation by NSS test. mESCs group displayed a significant benefit on NSS function (Fig. 4). Therefore, the transplanted group had shown lesser neuronal damage in the corpus striatum and cortex area induced by ischemia and cell. Cell treatment may change efficient improvement in the synapticity and locomotive enhancement of the motor function by implanted cell strategy [7]. Implanted cells may specifically differentiate into neuronal cell or glial cell line in the ischemic region of adult rats [8]. We found that majority of the grafted cells had a Nestin-positive marker and differentiated into glial cell or astrocytes expressing GFAP.

Fig. 2 Survival of mES cells in the lesion center expresses Nestine (yellow and red color: Nestinpositive neuronally differentiated transplanted cells) (400× magnification)

Fig. 3 Immunohistochemical staining of MCAO obtained at 28 days after transplantation of mESC (green dot: GFAP-positive astrocytes) (400× magnification)

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Fig. 4 Behavioral functional tests sham-control (PBS solution treated) group and embryonic stem cell treated groups were as follows the testing methods: motor test and adhesive-removal test

4 Conclusion This research suggests to understand a different intensity of dye the injured area, but the damaged area of the control group is almost bleached. The red viable tissue area of the control group is less found that the surrounding area of the injured area is wide. Thus, mESCs transplantation have access to crunch to intensive and transferred to new motor skills and contribute to the regeneration of nerve cells for the improvement of sensory functions. In conclusion, we proved that mESCs by intracerebral implantation survived and migrated to the injury site. We also found that implanted cells differentiated to induce motor improvement after ischemia. Stem cell therapy may be an excellent strategy for treatment of severe stroke patient. Potentially, stem cell transplantation may suggest an alternative cell based treatment for brain injury. Acknowledgements Funding for this paper was provided by Namseoul university year 2021.

References 1. Bursch, W., Ellinger, A.: Autophagy-a basic mechanism and a potential role for neurodegeneration. Folia Neuropathol. 43(4), 297–310 (2004) 2. Lu, L., Juan, C., Yong, M., Jianbin, L., Biansheng, J.: Transplantation of human umbilical cord blood mononuclear cells attenuated ischemic injury in MCAO rats via inhibition of NF-κB and NLRP3 inflammasome. Neuroscience 369(15), 314–324 (2018) 3. Zong, X., Wu, S., Li, F., Lv, L., Xu, T.: Transplantation of VEGF-mediated bone marrow mesenchymal stem cells promotes functional improvement in a rat acute cerebral infarction model. Brain Res. 1676, 9–18 (2017)

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4. Steiner, B., Roch, M., Holtkamp, N., Kurtz, A.: Systemically administered human bone marrowderived mesenchymal stem home into peripheral organs but do not induce neuroprotective effects in the MCAo-mouse model for cerebral ischemia. Neurosci. Lett. 513(1), 25–30 (2012) 5. Behrouzifar, S., Vakili, A., Bandegi, A.R., Kokhaei, P.: Neuroprotective nature of adipokine resistin in the early stages of focal cerebral ischemia in a stroke mouse model. Neurochem. Int. 114, 99–107 (2018) 6. Zhu, H., Lin, W., Zhao, Y., Wang, Z., Zhou, H.: Transient upregulation of Nav1.6 expression in the genu of corpus callosum following middle cerebral artery occlusion in the rats. Brain Res. Bull. 132, 20–27 (2017) 7. Linden, C., Plumier, J., Fassotte, L., Ferrara, A.: Focal cerebral ischemia impairs motivation in a progressive FR schedule of reinforcement in mice. Behav. Brain Res. 279, 82–86 (2015) 8. Watanabe, T., Nagai, A., Sheikh, A.M., Mitaki, S., Yamaguchi, S.: A human neural stem cell line provides neuroprotection and improves neurological performance by early intervention of neuroinflammatory system. Brain Res. 1631, 194–203 (2016)

A Review of Cancer Detection and Prediction Based on Supervised and Unsupervised Learning Techniques Priya Mishra, Brijesh Raj Swain, and Aleena Swetapadma

Abstract Cancer is a major disease which possesses multi-dimensional problems that the medical world finds difficult to overcome and solve comprehensively even today. The visible manifestation of cancer is known as tumor which is an abnormal growth of tissue in the body. The cancer diagnosis can be done in different ways such as the physical verification, tests like urine and blood tests to identify the cancer, using PET (positron emission tomography) scan, MRI (magnetic resonance imaging), CT (computerized tomography) scan images. For any kind of clinical decisions, it is important to know the accurate difference between the benign and malignant tumors which decides the risk factor attached to the individual kinds of tumors. Complex multi-dimensional medical data makes the application of conventional and statistical classification techniques difficult. To overcome the shortcomings of the conventional methods the need arises for development of techniques to handle the medical data which also helps in clinical decision support. Learning based techniques help to overcome the optimization problems faced while dealing the medical data. In this study, comparative analysis of learning based methods in various research works has been presented. Some of the new research directions in development of deep learning approaches in cancer detection on Breast, Lungs, Brain and Prostate have also been discussed. Keywords Cancer detection · Machine learning · Deep learning · CT-PET · MRI images

1 Introduction Over the past decades, there has been continuous evolution of cancer detection research. The doctors and the researcher nowadays apply different new techniques for treatment and detection of the cancer. One of the most fundamental challenging P. Mishra (B) · A. Swetapadma School of Computer Engineering, KIIT University, Bhubaneswar 751024, India B. R. Swain Department of Medicine, Siksha O Anusandhan University, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_3

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tasks for the researcher as well as for the physician is the increment of the accuracy outcome of the treatments. Foremost among these new techniques are deep learning methods which have become popular tools to identify the pattern and relationship between different complex medical datasets which provides for the higher accuracy in cancer detection. In this study use of some learning methods regarding classification and segmentation of various types of images used for cancer detection has been discussed. Also different methods which are used for different types of cancer detection, the used datasets and the overall performance of each method have been discussed. In this review, various methods of cancer diagnosis and detection have been introduced and compared with different types of cancer, using different image datasets. And a literature review of different types of cancer and their accuracy and performance has been discussed. Machine learning is the area of computer science that enables us to program the computers to learn with or without being explicitly supervised. It provides an efficient statistical tool through which data can be explored and analyzed. Learning is basically two types, supervised and unsupervised learning. In supervised learning, models learn about each type of data using the labeled dataset. On the basis of test data, the model is tested and predicted the desired output. Using the training algorithm model is trained. After the completion of the training process the model is tested using the test data for the identification of the shape. In unsupervised learning the unlabeled input data or raw data is fed to the interpretation phase of the model for identification of the hidden patterns. After the interpretation phase appropriate algorithm is applied on the hidden patterns. Finally, the algorithm categorized the data into groups according to the difference and similarities between the data and finally provides the output. Deep learning techniques is one of the subset of machine learning techniques where different architectures are used to extract high levels features and classify using multiple layers from the input. In deep learning architecture, input layer consist of multiple number of images (image set). The images set are connected with pre trained network to detect the image. In pre trained network there is a convolutional network which is used to prevent the downsizing of the images. The convolutional layer is combined with RELU which work as nonlinear activation function used for fast computation and high accuracy. There is also a max-pooling layer in pre trained network which is used to compute the maximum value for each patch of the feature map. After this stage, in fully connected layer the fine tuning is applied to classify the images. And finally in the output layer gives the classified output. Various supervised and unsupervised learning techniques have been applied for detection of cancer.

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2 Learning Based Methods and Their Use in Cancer Detection Machine learning is the area of computer science that enables us to program computers to learn with or without being explicitly supervised. It provides an efficient statistical tool through which data can be explored and analyzed. Learning is of two types, supervised and unsupervised learning.

2.1 Supervised Learning In supervised learning, models learn about each type of data using the labelled data set. Based on the test data, the model is tested and predicted the desired output. Using the training algorithm model is trained. After the model is trained completely, the model is tested using the test data for the identification of the shape [1].

2.2 Unsupervised Learning In unsupervised learning, the unlabeled input data or raw data is fed to the interpretation phase of the model for identification of the hidden patterns. After the interpretation phase appropriate algorithm is applied to the hidden patterns. Finally, the algorithm categorized the data into groups according to the difference and similarities between the data and finally provides the output [1].

2.3 Deep Learning It is one of the subsets of machine learning techniques where different architectures are used to extract high levels of features and classify using multiple layers from the input. In deep learning architecture, the input layer consists of multiple numbers of images (image set). The images set are connected with the pre-trained network to detect the image. In the pre-trained network, there is a convolutional network that is used to prevent the downsizing of the images. The convolutional layer is combined with RELU which work as a nonlinear activation function used for fast computation and high accuracy. There is also a max-pooling layer in the pre-trained network which is used to compute the maximum value for each patch of the feature map. After this stage, in a fully connected layer, fine-tuning is applied to classify the images and finally output layer gives the classified output. Various supervised and unsupervised learning techniques have been applied to the detection of cancer.

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3 Approaches Used for Detection of Different Types of Cancer Various researchers have used different approaches for detecting different types of cancers such as breast cancer, lung cancer, skin cancer, colon cancer, prostate cancer, brain cancer etc. Different learning based techniques are discussed below for different types of cancers.

3.1 Detection of Breast Cancer Meriemsebai et al. has suggested detection of breast cancer using histopathological images. Histopathological images are used for studying the microscopic examination of tissue in order to study the manifestations of disease. For studying a partially supervised deep learning framework has been developed with two parallel deep fully convolutional neural networks to implement huge number of datasets of mitosis detection [2]. Valkonen et al. had published about the detection of breast cancer where they have used mammographic X-ray imaging to detect the breast cancer in early stage. They have also used deep neural network for detection of automated epithelial cell masks using fluoro- chromogenic cytokeratin ki-67. Ki-67 was less than 14% at the time of initially classified and when that Ki-67 is greater than 14% it is reclassified. Ki-67 is put in the Immune Ratiowhole slide viewer as done by the author [3]. For Ki-67 labeling index is analyzed and arbitrary-shaped region are drawn by the operators (pathologist). Shu et al. have used region pooling structure on CNN implemented with INBREST and CBIS dataset. At a particular k-value (region structure) the accuracy has been achieved. The INBREST dataset achieves 0.923 accuracy and 0.934 AUC when the k-value is at 0.5 and the CBIS dataset achieves 0.762 accuracy and 0.838 AUC when the k-value is at 0.7 this shows the better performances [4]. For invasive ductal carcinoma detection in breast Brancati et al. has used residual CNN which is a part of convolutional auto encoder network also to classify lymphoma. The comparison was done by UNET AND RESNET. Based on 3-Layered CNN and ALEXNET various datasets are evaluated [5]. A stacked sparse Auto encoder framework is proposed by Xu et al. used a for automated nuclei detection of breast cancer. This method can pick up maximum level feature in an unsupervised manner of pixel intensity according to the authors. The performance achieved by SSAE and SMC are 88.84% for precision, 82.85% for Recall, and 84.49% for f-measures. The average precision is 78.83% [6]. As shown in Table 1 our study explores different types of deep learning techniques that are used for breast cancer detection. Many authors have taken the raw images segmented the images using the predicted mask. And evaluated the overall performance using different images of the breast.

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Table 1 Summary of breast cancer detection Author and year

Types of images used for analysis

Techniques used

Dataset used

Performance measures

Sebai et al. 2020 [2]

Histopathological

A partially supervised deep learning framework

2014 ICPR, AMIDA13

F1-scores = 0.575, 0.698

Qi 2019 [7]

Histopathological

A deep active learning framework

BREAKHIS

Accuracy = 66.67%

Yap 2018 [8]

Ultrasound images FCN-ALEXNET

B&K MEDICAL HAWK2012 and PANTHER 2002

F-measures = 0.91 and 0.89

Alom 2020 [9]

Histopathological

DCNN, IRRCNN, MITOSIS 2012, multi-task MITOSIS 2014, learning CWRU

F1-scores = 0.878, 0.910, 0.6516

Wang 2020 [10]

Histopathological

Deep-CNN and ensemble support vector machine

ICIAR2018

Accuracy = 97.70%

Cui 2019 [11]

Pathological images

CNN

CAMELYON17

Accuracy = 98.6%

Xu 2016 [6]

Histopathological

Stacked sparse autoencoder and deep learning

H&E STAINED

F-measures = 84.49%

Shu 2020 [4]

Mammographic image

Deep neural networks

INBRET, CBIS

Accuracy = 0.923 and 0.762

Saha 2018 [12]

FISH AND IHC images

HER2 deep neural HER2 network and LSTM

Accuracy = 98.33%

Wang 2019 [13]

Computer-aided diagnosis

ELM CLUSTERING and deep-CNN

ROC CURVE

Accuracy = 80.75

Valkonen 2020 [3]

Histopathology, digital pathology

Deep learning, image segmentation

ROC CURVE

Accuracy = 88%

CNN

D-IDC

Accuracy = 98.76%

Brancati 2019 [5] Histopathology

3.2 Detection of Prostate Cancer For prostate cancer detection Yang et al. has used the deep learning framework using sequential CEUS images. They have also performed 3-D convolution operations for feature extraction from spatial and temporal dimensions. Azizi et al. proposed the RNN (deep recurrent neural network) along with that LSTM (long short term memory) networks in separating cancer from benign tissue in the prostate

26

P. Mishra et al.

Table 2 Summary of prostate cancer detection Author and year

Types of images used Techniques used for analysis

Dataset used

Performance measures

Azizi 2018 [14]

MRI and ultrasound images

Deep recurrent network and LSTM

TEUS

Accuracy = 0.93 Auc = 0.96 Sensitivity = 0.76 Specificity = 0.98

Feng and Yang 2019 [16]

Contrast-enhanced ultrasound

CNN

CEUS

Accuracy = 93%

Kwak 2017 [15]

Computer-aided diagnosis, microscopy

CNN

TMA

Auc = 0.974

and achieve the highest accuracy. It has also achieved 96% of curve, 76% of sensitivity, 98% of specificity and 93% of accuracy using temporal enhanced datasets [14]. Tissue Microarray (TMA) datasets from contrast enhanced ultrasound were used. The comparison was done with different deep learning approaches. After the computational performs the CNN cancer detection takes 9800 s in training phase and 0.050). In addition, the average of male students’ religious well-being was 3.61 ± 1.38 points, which was higher than that of female students (M = 3.54 ± 1.12), but not statistically significant (t = 0.287, p > 0.050). On the other hand, the average of male students’ existential well-being was 4.63 ± 0.76 points, which was higher than that of female students (M = 4.31 ± 0.66), and this difference was also statistically significant (t = 2.385, p = 0.018). According to the type of religion, the average of spiritual well-being, religious well-being, and existential well-being showed a statistically significant difference, with the highest mean of spiritual well-being in all areas being Christianity and the lowest was analyzed as Buddhism (Table 2).

3.3 Attitude Toward Death and Demographic Characteristics As a result of the mean difference test for attitude toward death, male students’ mean attitude toward death was 2.82 ± 0.37 points, which was statistically significantly (t = 2.800, p = 0.007) higher than that of female students (M = 2.60 ± 0.52). There was no statistically significant difference in the mean of attitude toward death according to the type of religion as see following the Table 3. However, as a result of post hoc analysis using Fisher’s Least Significant Difference (LSD) test to account for multiple comparisons, shows that Christianity, Buddhism and Catholicism religious groups have a statistically significantly higher average of attitude toward death than other groups, indicating that they were positively aware of attitude toward death.

3.4 Influencing Factors on Attitude Toward Death For 179 nursing college students, the regression analysis of dummy control was conducted by controlling religion and gender with dummy variables as factors affecting their attitude to death. As a result, the correlation coefficient between independent variables was less than 0.8 so it was used for dummy regression analysis, and after removing outliers exceeding the absolute value of 3 when diagnosing cases, the analysis was conducted using the input method. First, as a result of testing the assumption of regression analysis, it was confirmed that the autocorrelation of the error using Durbin-Watson was 1.84, which was higher than the test statistic (1.76), and there was no autocorrelation, and independent variables had tolerance limits of 0.1 or higher and VIF-value was not greater than 10, so

180

Total

< 0.050, ** p < 0.010

2

Other

*p

5

92

No

13

Catholicism

Buddhism

68

Christianity

31

149

Female

n

Male

Variables

3.55

3.25

2.87

2.88

3.43

4.55

3.54

3.61

1.17

0.07

0.89

0.95

0.88

0.83

1.12

1.38 37.551**

0.287 0.000

0.776

p 4.63

4.37

4.15

4.30

3.68

4.20

4.55

4.31

0.69

0.35

0.68

0.41

0.58

0.70

0.66

0.76

SD

0.018

0.018

2.385* 3.073*

p

t/F

Existential well-being

t/F

M

SD

Religious well-being

M

Table 2 Spiritual well-being according to demographic characteristics (N = 179)

3.96

3.70

3.58

3.28

3.82

4.55

3.92

4.12

0.77

0.14

0.54

0.60

0.60

0.71

0.73

0.92

SD

Spiritual well-being M

26.251**

1.316

t/F

0.000

0.190

p

186 W.-H. Moon and S.-Y. Yun

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187

Table 3 Attitude toward death according to demographic characteristics (N = 179) Variables

Categories

n

Attitude toward death M

Gender

Male

p

2.800**

0.007

1.950

0.104

31

2.82

0.37

2.60

0.52

Christianity

68

2.76

0.49

Catholicism

13

2.66

0.53

5

2.72

0.48

92

2.54

0.50

Buddhism No

*p

t/F

149

Female Religion

SD

Other

2

2.56

0.06

Total

180

2.63

0.50

< 0.050, ** p < 0.010

there was no problem of multi-collinearity. In addition, as a result of residual analysis, the linearity of the model, normality of the error term, and equal variance were confirmed. As a result of analyzing the regression model, the model was statistically significant (F = 3.519, p = 0.001). Among the controlled dummy variable religions, Christianity (B = 0.247), Catholicism (B = 0.167), Buddhism (B = 0.280), Other (B = 0.047) was found to have a more positive effect on attitude toward death than the non-religious group. In addition, males (B = 0.111) had a higher attitude toward death than females in the control dummy variable gender. And under the control of dummy variables, the lower the religious well-being (B = −0.055) among the independent variables, the higher the existential well-being (B = 0.195), the higher the attitude toward death. The controlling variable that had the greatest influence on the attitude toward death was Christianity (β = 0.239), and the existing well-being (β = 0.267), among the independent variables. Attitude toward death was explained by 10.2% with 5 dummy variables and 3 independent variables (F = 3.519, p = 0.001) (Table 4).

4 Conclusion Existential well-being was higher in men than in women, and this difference was statistically significant. Depending on the type of religion, the mean of spiritual well-being differed statistically significantly, and the well-being of all areas was the highest in Christianity and the lowest in Buddhism. Although men students had a statistically significantly higher attitudes toward death than women students, the average attitude toward death was statistically significant, depending on the type of religion, the average of attitudes to death is high, indicating that religious groups are positively aware. It was also confirmed that the

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Table 4 Influencing factors on attitude toward death (N = 179) Model (Constant) Age Religious well-being

B

S.E

β

t

1.357 0.344 0.023 0.011

p

Tolerance VIF

3.944 0.000 2.157 0.032 0.973

1.027

−0.055 0.043 −0.128 −1.277 0.203 0.504

0.155

1.983

Existential well-being

0.195 0.056

0.267

3.464 0.001 0.849

1.178

Dummy-Christian

0.247 0.104

0.239

2.38

0.018 0.501

1.995

Dummy-Catholic

0.167 0.143

0.087

1.167 0.245 0.912

1.096

Dummy-Buddhist

0.280 0.221

0.092

1.265 0.208 0.95

1.052

Dummy-Other

0.047 0.341

0.01

0.138 0.890 0.982

1.018

Dummy-Male

0.111 0.098

0.083

1.128 0.261 0.937

1.067

Durbin-Watson’s d = 1.84(1.59 ≤ d ≤ 1.76), adjR2 = 0.102, F = 3.519, p = 0.001

lower the Religious well-being, the higher the Existential well-being, the more positive the attitude toward death. The most influential factor in attitude toward death was Christianity and the Existential well-being of independent variables. Therefore, due to the nature of nursing college students, they need to be aware of the attitude toward death positively as they can often experience the death of the nursing target in the future, so it is necessary to improve the spiritual wellbeing identified as an influential factor. To this end, it may be necessary to develop educational programs such as smart healthcare that establish the value of death and philosophical consideration of existence during college life.

References 1. Kang, E.H., Kim, M.Y.: Good death, self-esteem, and attitude to life among nursing students. J. Korean Soc. Health Sci. 14(1), 1–10 (2017) 2. Kim, S.Y.J.: The Growth and Development of Youth and Nursing. Soomoonsa (2004) 3. Yoo, J.E., Cheon, Y.R.: Investigation into the relationship among religious well-being, existential well-being, depression, and life satisfaction of Christian college students. Korean Soc. Study Christ. Relig. Educ. 0(58), 285–310 (2019) 4. Watson, J.: Nursing: Human Science and Human Care, pp. 55–56. National League for Nursing, New York (1988) 5. Kim, H.S.: Effect on influence the attitude of death of the old ages for afterlife view and death preparation and spiritual wellbeing. J. Korea Contents Assoc. 16(7), 492–503 (2016) 6. Kim, K.H., Kim, K.D., Byun, H.S., Chung, B.Y.: Spiritual well-being, self esteem, and attitude to death among nursing students. J. Korean Oncol. Nurs. 10(1), 1–9 (2010) 7. Kim, J.H., Min, K.H.: Predictors of death attitude and death competency among the elders. Korean J. Soc. Pers. Psychol. 24(1), 11–27 (2010) 8. Kim, M.S.: Philosophical studies on cognition and attitude of Koreans towards death. J. Confucian. Res. Inst. 22(1), 73–108 (2010) 9. Kim., S.Y., Hur, S.S., Kim, B.H.: Study of subjective view on the meaning of well-dying held by medical practitioners and nursing students: based on Q-methodology. Korean J. Hosp. Palliat. Care 17(1), 10–17 (2014)

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10. Hong, M.S.: A study on attitude of nurses toward euthanasia. J. Korea Community Health Nurs. Acad. Soc. 14, 446–463 (2000) 11. Oh, J.T.: The Last Present. Sejong Books, Seoul (2007) 12. Kim, S.N., Choi, S.O., Lee, J.J., Shin, K.I.: Effects of death education program on attitude to death and meaning in life among university students. Korean J. Health Educ. Promot. 22(2), 141–153 (2005) 13. Kim, S.H., Kim, D.H.: Nursing students’ attitude to death, death-related education experience and educational needs. J. Korean Acad. Soc. Nurs. Educ. 17(3), 405–413 (2011) 14. Jo, K.H.: The meaning of dignified dying perceived by nursing students. J. Korean Acad. Soc. Nurs. Educ. 16(1), 72–82 (2010) 15. Paloutzian, R., Ellison, C.: Loneliness, spiritual well-being and the quality of life. In: Peplau, L., Perlman, D. (eds.) Loneliness: A Source book of Current Theory, Research and Therapy, pp. 224–237. Wiley, New York (1982) 16. Park, J.A.: Relationship to resilience, spiritual well-being and parent attachment comparison among resilient adolescents, at-risk adolescents and competent adolescents. Masters dissertation, Ewha Women’s University, Seoul, Korea (2003) 17. Thorson, J.A., Powell, F.C.: Elements of death anxiety and meaning of death. J. Clin. Psychol. 44(5), 691–701 (1988) 18. Park, S.C.: A study on the death orientation of hospice volunteers. J. Korean Acad. Nurs. 22(1), 68–80 (1992)

Development of Smart Healthcare Cosmetic Ingredients Using Lactic Acid Fermented Coffee Hye-jin Kwon

Abstract This study attempted to develop a new cosmetic ingredient by enhancing the physiological activity of coffee. Three types of lactic acid bacteria (Leuconostoc holzapfelii, Weissella cibaria, Lactobacillus plantarum) were cultured and used on green coffee beans. The extracts prepared from three types of lactic acid bacteria solid fermented green coffee beans showed higher total polyphenol and flavonoid content and DPPH free radical scavenging ability than non-fermented coffee beans. In addition, it effectively inhibited the collagenase enzyme. Therefore, this study is thought to present data on the possibility of a new smart healthcare cosmetic ingredient through fermentation of lactic acid bacteria by comparing the antioxidant properties of cosmetic ingredients made from green beans fermented with lactic acid bacteria and confirming the organoleptic properties. Keywords Fermented coffee · Antioxidant · Lactic acid · Physiological activity · Smart healthcare

1 Introduction As biocosmetics, a new paradigm in the cosmetic industry, has recently emerged, various studies on metabolites naturally produced by living organisms are being conducted. Biocosmetic uses biotechnology to maximize efficacy through biological materials based on the biology principle. As materials for biocosmetics, enzymes, microorganisms, and natural plants are used, and peptides, stem cells, and RNAs are also used [1]. Fermentation, which is one of the technologies used in biocosmetics, is a phenomenon in which sugars are decomposed into ‘nontoxic’ by microorganisms, and the particles of molecules become smaller by the action of decomposing enzymes, thereby increasing skin absorption. In addition, not only the nutritional components of the raw materials themselves are strengthened, but also nutrients beneficial to the

H. Kwon (B) Department of Chemical Engineering, Soongsil University, Dongjak-gu, Seoul 06978, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_13

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skin such as vitamins and organic acids are additionally produced during the process of culturing the fermenting microorganisms [2]. Recently, as the physiological activity of the fermentation process has been known, fermented foods are recognized as health functional foods worldwide [3]. In particular, lactic acid bacteria are known as very beneficial microorganisms that can be used by humans, and probiotics have the effect of improving intestinal function [4]. Has been reported since long ago, and has been directly and indirectly related to human life according to its characteristics ranging from fermented dairy products to various types of paste, kimchi, fermented sausage, pharmaceuticals, and feed additives for livestock. About 300–400 species are known [5]. In addition, lactic acid bacteria not only give fermented food its unique flavor and excellent preservation, but also alleviate lactose intolerance, intestinal function, inhibit the growth of pathogenic bacteria, reduce cholesterol, anticancer, antiviral, and antistress. While known to exhibit various physiologically active functions [6], yogurt production using cholesterol-lowering lactic acid bacteria [7], and lactic acid fermentation through lactic acid bacteria fermentation Various studies have been reported on the possibility of enhancing physiological activity through fermentation of lactic acid bacteria, such as the functional enhancement effect of kimchi [8, 9]. When this technology is used in cosmetics, the final product has the following changes. ➀ The molecular structure becomes smaller. As the molecular structure becomes smaller enough to be absorbed into the skin, absorption increases. ➁ nutrient content increases. It becomes more useful as new organic matter formations are created. ➂ A new substance that is good for the skin is created. Metabolites (amino acids, vitamins, and functional substances produced) eaten and excreted by microorganisms are the best substances that are easy to absorb into the skin. ➃ It is easy to preserve. Nutrients are not damaged. Resistant to heat and carbon dioxide, and increases preservation and safety. ➄ The bio-converted material detoxifies various toxicity and has almost no side effects. Toxicity is alleviated through the process of biotransformation. ➅ Skin absorption rate increases [10]. It is known that coffee has a high content of antioxidants such as polyphenols, so it has a high ability to scavenging free radicals that cause cell damage. In addition, as it is known to have an excellent protective effect on diabetes, cholesterol, heart disease, etc., many studies have been conducted on the pharmacological effect of coffee beyond taste foods. However, negative studies have also been reported that caffeine in coffee can cause cardiovascular and coronary artery disease. Therefore, various processes to maximize useful ingredients and reduce harmful ingredients are required. Therefore, in this study, we tried to evaluate the possibility of application as a biocosmetic material through antioxidant and anti-aging experiments using a fermentation process to enhance the physiologically active components of coffee.

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2 Materials and Method 2.1 Fermented Coffee Manufacturing Process Green coffee beans (Brazil, Colombia, Costa Rica, Kenya) were purchased from Hwaseong, Korea. Green coffee beans and water were immersed in a ratio of 1:1.5 for 1 h and then increased at 121 °C for 1 h. After that, through a cool-down process, three kinds of Leuconostoc holzapfelii, Weissella cibaria, and Lactobacillus plantarum were inoculated into green coffee beans, followed by fermentation at 37 °C for 24 h. After fermentation, it was washed with water and dried in a hot air dryer at 45 °C for 24 h. After drying, it was powdered, diluted 10 times in 80% ethanol, and extracted for 4 h at 40 °C and 180 rpm. The extract was used as an experimental sample after removing the residue by using a 0.45 syringe filter.

2.2 Measurement of Polyphenol, Flavonoid Content and DPPH Radical Scavenging Ability For the total phenol content, 12.5 µL of Folin-Ciocalteu reagent was mixed with 200 µL of the sample extracted according to the Folin-Denis method, stirred, and allowed to stand at room temperature for 120 min to react. The total flavonoid content is determined by adding 0.1 ml of 10% aluminum nitrate, 0.1 ml of 1 M potassium acetate, and 4.3 ml of ethanol to 0.5 ml of the sample extracted according to the Davis method, and reacting at room temperature for 40 min to measure the absorbance at 450 nm. The 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging ability was modified by Blois’ method to measure the reducing power of each sample as an effect on electron donation capabilities. After adding 150 µL of 0.15 mM DPPH solution (dissolved in 99% methanol) to 50 µL of the sample, it was allowed to stand at room temperature for 30 min, and then the change in absorbance at 517 nm was measured using a spectrophotometer.

2.3 Measurement of Collagenase Inhibitory Activity Azocoll (Azo dye-impregnated collagen, A4341, Sigma), a substrate of collagen enzyme, was homogenized by adding 900 µL of 0.1 M Tris-HCl (pH 7.0) as a buffer solution to 1 mg, and then collagenase type I at a concentration of 200 ml. After preparing an enzyme (C0130, Sigma) solution, 100 µL of Collagenase type I enzyme (concentration of 200 units/ml) was added to prepare a total reaction solution of 1 ml. After reacting at 43 °C. for 1 h, when the reaction was completed, centrifugation was performed at 3000 rpm for 10 min, and then the undigested collagen was precipitated and the supernatant containing the degraded collagen was separated. The supernatant

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was measured for absorbance at 550 nm. As a control, absorbance was measured by adding the same amount of the reaction buffer solution as the enzyme instead of the enzyme solution, and the value of the sample itself was also measured and corrected when calculating the enzyme activity.

3 Results and Discussion 3.1 Polyphenol, Flavonoid Content and DPPH Radical Scavenging Ability Reactive Oxygen Species (ROS) attack unsaturated fatty acids, which are components of the cell biomembrane, and cause oxidation reactions with lipids, accumulating lipid peroxide in the body, reducing human function and causing various diseases such as adult diseases, and aging. It is known to be. This is also a cause of inflammation as well as damage to cellular tissues by free radicals with unpaired electrons generated from oxygen. DPPH (1,1-Diphenyl-2-Picrylhydrazyl) is itself very stable Free Radical. When it encounters a substance with antioxidant activity, the radicals are scavenged and the blue-blue color of DPPH becomes pale so that the antioxidant activity can be analyzed with the naked eye. Therefore, it is widely used to analyze antioxidants from various natural materials. Peroxide in the body is regulated by antioxidants such as Glutathione Peroxidase, Catalase, Superoxide Dismutase, Vitamin C, Vitamin E, and Glutathione. DPPH is an indicator of antioxidant activity against phenolic chemicals such as Phenol and Flavonoid. By donating electrons to free radicals, DPPH can be used as a measure to prevent diseases and aging in the human body by inhibiting the fatty acid formation and aging. The electron donating ability is used as an indicator of antioxidant activity that prevents oxidative stress caused by ROS [11], and is also used as a measure of antioxidant activity against phenolic substances such as phenolic acid and flavonoids [12]. Therefore, the increase of polyphenolic substances in food is strong. It reflects the antioxidant power, and such physiological activity is known to be mainly due to the reducing Table 1 Effect of antioxidant 1 µg/ml

10 µg/ml

100 µg/ml

1000 µg/ml

Ferm.

50.00 ± 0.61

56.00 ± 0.05

83.60 ± 0.03

90.39 ± 0.19

Non

11.00 ± 0.54

28.11 ± 0.04

56.70 ± 0.03

79.83 ± 0.16

Ferm.

0.60 ± 0.26

1.50 ± 0.47

21.60 ± 0.72

40.10 ± 0.06

Non

90.39 ± 0.19

90.39 ± 0.19

90.39 ± 0.19

90.39 ± 0.19

Ferm.

40.21 ± 0.24

48.01 ± 0.63

50.55 ± 0.33

57.32 ± 0.13

Non

3.01 ± 0.32

8.04 ± 0.11

15.42 ± 0.25

22.13 ± 0.85

Concen DPPH radical Flavonoid Polyphenol

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power [13]. Table 1 shows the total polyphenol content, flavonoid content, and electron donating ability of fermented coffee and general coffee extract. In all items, the antioxidant activity of the fermented extract was excellent.

3.2 Collagenase Inhibitory Activity Collagen is the main component of connective tissue and is mainly found in bones and skin, but it is a component distributed throughout our body such as joints, membranes of each organ, and hair. In addition, collagen functions such as skin firmness, connective tissue bonding, cell adhesion, and inducing cell differentiation. More than 90% of the dermal layer is composed of collagen, and collagen plays a role in protecting and maintaining the skin against external irritation by giving the skin tension and strength. A typical symptom of skin aging is the occurrence of fine lines and wrinkles. This can be said to be due to the remarkable decrease in collagen, the main protein of collagen in the skin dermal tissue. The decrease due to collagen degradation is caused by the destruction of the connective tissue that maintains the elasticity of the skin, causing wrinkles, decreased elasticity, and sagging of the skin. As a result of analyzing the collagenase inhibition rate (%) of fermented coffee and general coffee extract, the general extract showed an inhibition rate of 26.23 ± 0.32, and the microbial fermented extract showed the highest inhibition rate of 51.34 ± 0.21 (Fig. 1). Antioxidant active ingredients in the form of glycosides exhibit higher antioxidant activity when sugars are removed by glycoside hydrolase [12]. It has been reported that the activity is improved by about 16–40% [13]. In addition, it has been reported that active ingredients newly generated through fermentation and secondary metabolites produced by lactic acid bacteria act positively on antioxidant-related enzymes [11]. Fig. 1 Collagenase inhibition of the extracts

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Therefore, in this study, it is thought that antioxidant properties and activity and collagenase inhibitory activity were enhanced due to the fermentation properties of lactic acid bacteria in green coffee beans. However, in order to identify the physiological activity and antioxidant components of green coffee beans by fermentation of lactic acid bacteria, additional studies on the purification and structure of the active ingredients are considered necessary.

4 Conclusion In this study, green coffee beans fermented with lactic acid bacteria were used to analyze antioxidant and collagenase activity inhibition to analyze their suitability as a cosmetic ingredient. The longer the fermentation time, the greater the brownness and antioxidant effect of green beans. In particular, in the collagenase inhibitory activity, the efficacy of the general coffee extract and the fermented extract showed a two-fold difference. Therefore, fermented coffee is expected to be suitable as a natural smart healthcare cosmetic ingredient. Recently, as interest in smart healthcare has increased, interest and demand for functional raw materials are increasing. In particular, this trend is becoming more and more common because everyone is unwilling to use synthetic materials. The field of interest and use of natural herbs continues to grow as a dietary supplement and cosmetic ingredient. But the most important thing is the safety issue. These natural products have a number of advantages. However, certain individuals also have disadvantages such as toxicity or side effects. Therefore, continuous and systematic research on the stability of raw materials and herbal medicines from natural products should be conducted.

References 1. Frisullo, P., Laverse, J., Barnaba, M., Navarini, L., Del, N., M.A.: Coffee beans microstructural changes induced by cultivation processing: an X-ray microtomographic investigation. J. Food Eng. 102, 175–181 (2012) 2. Cheung, L.M., Cheung, P.C.K., Ooi, V.E.C.: Antioxidant activity and total phenolics of edible mushroom extracts. Food Chem. 81, 249–255 (2003) 3. Bang, J.H., Shin, H., Choi, H.J., Kim, D.W., Ahn, C.S., Jeong, Y.K., Joo, W.H.: Probiotic potential of Lactobacillus isolates. J. Life Sci. 22, 251–258 (2012) 4. Lim, Y., Shin, J.Y., Kim, H., Back, G.H., Yu, K.W., Jeong, H.S., Lee, J.S.: Anti-adipogenic effect of fermented coffee with monascus ruber mycelium by solid-state culture of green coffee beans. J. Korean Soc. Food Sci. Nutr. 43, 624–629 (2014) 5. Park, S.J., Lee, I.S., Lee, S.P., Yu, M.H.: Inhibition of adipocyte differentiation and adipogenesis by supercritical fluid extracts and marc from Cinnamomum verum. J. Life Sci. 23, 510–517 (2013) 6. Kim, D.H., Yeon, S.J., Jang, K.I.: Quality characteristics and antioxidant activity of espresso coffee prepared with green bean fermented by lactic acid bacteria. J. Korean Soc. Food Sci. Nutr. 45, 1799–1807 (2016)

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7. Ann, Y.G.: [Lactic acid bacteria] Probiotic lactic acid bacteria. Korean J. Food Nutr. 24, 817–832 (2011) 8. Yang, M.C., Jeong, S.W., Ma, J.Y.: Analysis of constituents in Sipjundaebo-tangs fermented by lactic acid bacteria. Korean J. Microbiol. Biotechnol. 39, 350–356 (2011) 9. Cho, A.S., Jeon, S.M., Kim, M.J., Yeo, J., Seo, K.I., Choi, M.S., Lee, M.K.: Chlorogenic acid exhibits anti-obesity property and improves lipid metabolism in high-fat diet-induced obese mice. Food Chem. Toxicol. 48, 937–943 (2010) 10. Park, J.Y.: Antioxidant activities and quality characteristics of pan bread with green coffee bean powder, pp. 26–42. MS thesis, Sejong University, Seoul, Korea (2013) 11. Bhumiratana, N., Adhikari, K., Chambers, E.: Evolution of sensory aroma attributes from coffee beans to brewed coffee. LWT-Food Sci. Technol. 44, 2185–2192 (2011) 12. Kim, J.H., Lee, W.J., Cho, Y.W., Kim, K.Y.: Storage-life and palatability extension of Betula platyphylla sap using lactic acid bacteria fermentation. J. Korean Soc. Food Sci. Nutr. 38, 787–794 (2009) 13. Macrae, R.: Nitrogenous compounds. In: Clarke, R.J., Macrae, R. (eds.) Coffee Volume 1: Chemistry, pp. 115–152. Elsevier Applied Science, Essex (1985)

Ranking of SARS-CoV-2 Vaccines with Reference to India Proshikshya Mukherjee, Sudhir Kumar Rath, Sibanand Mishra, and Prasant Kumar Pattnaik

Abstract The “Severe Acute Respiratory Syndrome-Corona Virus-2” (SARS-CoV2) disease gives a challenge to the healthcare facilities and economic systems across the world. To overcome this COVID-19 pandemic different vaccines are produced across the world like Pfizer-BioNTech, Moderna, AstraZeneca-University of Oxford, Johnson & Johnson, Russia’s Sputnik V, Sinovac Biotech, Novavax, CanSino Biologics, and COVAXIN Bharat Biotech. These vaccines are injected to improved the immunity to fight against the SARS-CoV-2 infection. All these vaccine storage, efficiency, price are different and all vaccines work different variants of the virus. Some of the vaccines are mRNA type, Some are inactivated SARS-CoV-2, some are different. Our work is using the Multi-Criteria Decision Making (MCDM) technique, we ranking the vaccine with respect to different criteria. Keywords SARS-CoV-2 · COVID-19 · MCDM · Pfizer-BioNTech · Moderna · AstraZeneca-University of Oxford · Johnson & Johnson · Russia’s Sputnik V · Sinovac Biotech · Novavax · CanSino Biologics · COVAXIN Bharat Biotech

1 Introduction The SARS-CoV-2 is contagiously, highly effected on the global population’s health and global economy [1]. The mortality rate and the spreading rate of this virus are continuously changing. According to the WHO (World Health Organization) in December 2020, 216 countries are involved in the COVID-19 pandemic and 80,161,578.00 people are affected with a 2.19% (1,756,379.00) fatality rate [2]. On 24 November 2020 different types of COVID variant comes across the world. The Wuhan is the first strain of the COVID virus that is identified from China in Wuhan city. In December 2020, B.1.1.7 was first identified in the UK according to the CDC. In October 2020, in South Africa B.1.351 variant was found and end of January 2021 P. Mukherjee (B) · S. K. Rath · S. Mishra · P. K. Pattnaik KIIT Deemed to be University, Bhubaneswar, Odisha, India S. K. Rath e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_14

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it is double muted in different parts of the world. In Japan P.1 variant was found in January 2021. Then this variant is muted and muted variant mainly observed in the U.S. On 25 February 2021, B.1.526 variant was founded in New York then muted in South Africa. The muted variant called in E484K and 59 different lineages virus are found. B.1.427/B.1.429 variant was found in California after mutation it is called L452R. In India double mutant variant B.1.617 is mainly founded. India variants have two spike proteins one is 452 and another one is 484 [3]. The people health care during this pandemic is very challenging and the people are facing lockdown, stress, anxiety, and life uncertainty because the SARASCoV-2 do not have any particular medicines and vaccine or any specific treatment [4]. Recently on 11 December 2020, the Pfizer-BioNTech vaccine was prepared and sent to the US FDA (Food and Drug administration) for EUA (Emergency Use Authorization) [5]. The vaccine is the mRNA vaccine. Once it is injected cells turn out to spike protein and it is triggered by the immune system of the body to find out the virus. The efficiency of the virus is 95% after the phase 3 trial. In the US it is authorized for 12–15 year age people [6]. On 19 December 2020, FDA was authorized another vaccine Moderna. It is also mRNA type of vaccine. From trial 1 it is 94.5% efficient and after phase 2/3 trial (May 2021) it is 93% effective. The phase 2/3 trial was done over 12–17 age group of the children [7]. On 12 January 2021, Europe authorized AstraZeneca with Oxford University vaccine. It is deployed a replication-deficient chimpanzee virus. After the vaccine trigger into the body cells generate the spike protein and stimulated our immune system to attack the COVID-19 virus [3]. On 15 November 2020, Johnson & Johnson introduced a COVID-19 vaccine. AdVac technology is used for the vaccine preparation. The Efficiency of vaccine is 66% [8]. On 11 November 2020, Russia developed 1st COVID-19 vaccine Sputnik V in this global pandemic. After the phase 3 trial 92% efficiency of this vaccine. Inactivated SARS-CoV-2 virus used in this vaccine [9]. On 28 January 2021 another COVID-19 vaccine Novavax introduced. The efficiency of this vaccine is 89.3%. In this vaccine is prefusion spike protein create using the company’s recombine nanoparticle technology and it is a proprietary saponin-based Matrix-M adjuvant [10]. On 8 February 2021, the Chinese military developed the CanSino Biologics vaccine. The efficiency of this vaccine 65.7% and after the phase 3 trial the efficiency is 90.98% [11]. On 22 April 2021, Bharat Biotech developed COVAXIN. 100% effective against severe COVID19 virus infection. The vaccine was developed inactivated SARS-CoV-2 virus using Whole-Virion Inactivated Vero Cell platform [12]. The entire chapter is divided into the following way. The first section is the introduction of the chapter. The comparison of the vaccine is shown in Sect. 2. In Sect. 3 using the MCDM technique, the ranking selection is done and the section name is result and discussion. The conclusion of the chapter is disused in Sect. 4.

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2 Comparison of the Vaccine The 9 vaccines are compared with respect to number of doses, price, efficiency and variant, Storage in Table 1. The comparison table shows in below. From Table 1 we have got four criteria for the selection of the vaccine. The four criteria are price, efficiency, temperature of the vaccine storage and the number of variants the vaccines are worked. The MCDM technique is used for criteria selection purposes. In this process, ranking is calculated from the Matrix. In the next section, the process will be discussed. Table 1 Vaccines comparison Vaccine

Price

No. of doses Efficiency

Variant

Storage

P(V1 )

$19.50/dose

2, 21 Days apart

95% effective mild, moderate and 100% prevented hospitalization

South African, −94 °F Latin American and moderately work UK variant

M(V2 )

$25–$37/dose

2, 28 days apart

95% effective mild, moderate and 100% prevented hospitalization

South African, 36–46 °F and Latin American −4 °F for six and moderately month work UK variant

A(V3 )

$2.15 in the EU, 2, 28 days $3–4 in UK and apart $5.25 in South Africa

70% overall

South African variant little effective, Brazil and UK variant effectively work

J(V4 )

$10/dose

1

66% effective in symptomatic cases and 100% prevented hospitalization

UK variant Unknown highly effective less effective on South Africa and Latin America strain

R(V5 )

$10/dose

2, 21 days

91.4%

Rusian variant

Unknown

S(V6 )

$60/dose

2

50.38–91.25%

Brazil

Unknown

N(V7 )

$16

2

89.3%

UK and South African variant

2–8 °C stable

C(V8 )

Unknown

1

65.7–90.98%

Unknown

Unknown

B(V9 )

About $2

2, 28 days apart

100% against Brazil, Hungary severe infection, and U.K. types 78% effective and Wuhan mild, severe and moderate symptoms and 70% against asymptomatic

Normal refrigerate 36–46 °F

Sub-zero storage not required and 2–8 °C

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3 Result and Discussion In the MCDM method, we have four criteria efficiency (C1 ), price (C2 ), storage (C3 ), and the variant (C4 ) and nine alternatives like P(V1 ), M(V2 ), A(V3 ), J(V4 ), R(V5 ), S(V6 ), N(V7 ), C(V8 ), B(V9 ). Figure 1 shows the entire MCDM structure. The figure structure is like tree architecture so AHP (Analytical Hierarchy Process) MCDM method is used for the goal calculation. Our main aim is to find a vaccine that is widely used across the world [13]. AHP method ranking calculation Likert scale we have used for priority selection. Table 2 shows the relative importance scale of criteria selection. Step 1: Pairwise comparison matrix calculation from Table 2 for criteria selection. Table 3 shows the priority calculation matrix. Equations 1–4 shows the priority calculation of the pairwise comparison matrix.      C1 = 1 1 + 1 2 + 1 4 + 1 5 = 0.513

(1)

 C2 = 1 (2 + 1 + 3 + 5) = 0.091

(2)

   C3 = 1 4 + 1 3 + 1 + 4 = 0.107

(3)

Fig. 1 Architecture of vaccine finding technique

Table 2 Relative important scale

Variable

Value

Same priority

1

Very week priority

2

Week priority

3

Strong priority

4

Very strong priority

5

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Table 3 Priority calculation of pairwise comparison matrix C1 C1

1

C2

2

C3

4

C4

5

C2  1 2

C3  1 4

C4  1 5

0.513

Priority

1  1 3  1 5

3

5

0.091

1  1 4

4

0.107

1

0.155

    C4 = 1 5 + 1 5 + 1 4 + 1 = 0.155

(4)

After the pairwise comparison matrix we have to calculate the consistency index (CI). Equation 5 shows the CI. CI =

8.404 − 4 ‫ג‬max − n = = 1.47 n−1 3

(5)

Step 2: Pairwise comparison matrix calculation for the alternatives. Table 4 shows the pairwise comparison matrix and the priority of the matrix. Step 3: In this step pairwise comparison matrix calculation with respect to different criteria. Tables 5, 6, 7 and 8 shows comparison table respected to the efficiency (C1 ), price (C2 ), storage (C3 ) and the variant (C4 ) respectively. Step 4: Overall rank calculation from the alternatives {V1 , V2 , V3 , V4 ,…., V9 }. Equation 6 shows the ranking of the matrix. Table 4 Pairwise comparison matrix of alternative V1 V2 V3 V4 V5 V6 V7 V8 V9

V1

V2

V3

V4

V5

V6

V7

V8

V9

Priority

1  1 5  1 4  1 4  1 3  1 2  1 4  1 2  1 4

5

4

4

3

2

4

2

4

0.2830

1  1 3  1 3  1 2  1 2  1 3  1 2  1 3

3

3

2

2

3

2

3

0.1132

1  1 4  1 3  1 4  1 2  1 3  1 5

4

3

4

0.1013

3

4  1 2

3  1 4  1 2

5

1  1 3  1 4

2  1 2  1 3

5  1 4

0.0532

2

2

4

0.0435

2

3

2

2

1

4  1 5

0.0736

4  1 5

1  1 2  1 4

5

1

0.0378

1 2

4

1  1 2  1 2  1 4

0.0678

0.0563

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Table 5 Pairwise comparison matrix with respect to efficiency (C1 ) V1

V2 5

V2

1  1 5

V3

3

2

V4

2

3

V5

4

4

V6

2

2

V7

4

4

V8

3

3

V9

5

5

V1

1

V3  1 3  1 2

V4  1 2  1 3

V5  1 4  1 4

1  1 4  1 5

4

5

1  1 4

4 1

V6  1 2  1 2  1 2  1 2  1 2

V7  1 4  1 4

V9  1 5  1 5

Priority

5

0.023

5

0.0606

5

0.0658

5

0.0603

1

V8  1 3  1 3  1 3  1 2  1 3  1 3  1 3

2  1 4

2

2

1

4  1 4  1 4  1 4

4

4

4

5

0.0376

3  1 5

2  1 5

3  1 5

3  1 5

3  1 5

1  1 5

5

0.0385

1

0.0833

0.1149 0.2804

Table 6 Pairwise comparison matrix with respect to price (C2 ) V1 V1

1

V2  1 2

4

3

4

V2

2  1 4  1 3  1 4

1  1 5  1 4  1 4

5

4

4

1  1 4  1 4

4

4

1

1

1

1

2  1 3  1 2  1 5

2  1 3  1 2  1 5

2  1 3  1 2  1 5

2

2

3  1 2  1 5

3  1 2  1 5

V3 V4 V5 V6 V7 V8 V9

V3

V4

V5

V6  1 2  1 2  1 2  1 2  1 2

V7

V8

V9

Priority

3

2

5

0.0435

3

2

5

0.0377

3  1 3  1 3

2

5

0.0501

2

5

0.09382

2

5

0.0945

1  1 4  1 2  1 5

4

2

5

0.04545

1  1 2  1 5

2

5

0.0655

1  1 5

5

0.1053

1

0.38468

Table 7 Pairwise comparison matrix with respect to storage (C3 ) V1 V2 V3 V4 V5 V6 V7 V8 V9

V1

V2

V3

V4

V5

V6

V7

V8

V9

Priority

1  1 2  1 5  1 2  1 2  1 2  1 4  1 2  1 5

2

5

2

2

2

4

2

5

0.04

1  1 5  1 2  1 2  1 2  1 4  1 2  1 5

5

2

2

2

4

2

5

0.0426

1  1 2  1 2  1 2  1 4  1 2  1 5

2

2

2

4

2

5

0.05433

1

1

1

4

1

5

0.0689

1

1

1

4

1

5

0.0689

1  1 4

1  1 4

1  1 4

4

5

0.0689

1

1  1 2

5

0.125

1  1 5

1  1 5

1  1 4

2  1 4

1  1 5

5

0.08

1

0.38468

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Table 8 Pairwise comparison matrix with respect to variant (C4 ) V1

V2

V1

1

1 1

V3  1 4  1 4

V4  1 4  1 4

V2

1

V3

4

V4 V5

4

1

1

4

4

1

3

3

3

V6

3

3

V7

4

4

V8

2  1 5

2  1 5

V9

1

V5  1 3  1 3  1 3  1 3

V6  1 3  1 3  1 3  1 3

3

1

1

3

3

1

4

4

2

2  1 5

2  1 5

2  1 5

⎛

V8  1 2  1 2  1 2  1 2  1 2  1 2

V9

Priority

5

0.1143

5

0.1143

5

0.0609

5

0.0609

5

0.05

1

V7  1 4  1 4  1 4  1 4  1 2  1 2

5

0.05

2

1

1

5

0.0370

2  1 4

1  1 4

1  1 5

5

0.0526

1

0.3846

⎞

⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎜ 0.0849 ⎟ ⎟ ⎜ ⎜ 0.165 ⎟ ⎟ ⎜ ⎜ 0.0316 ⎟ ⎟ ⎜ ⎜ 0.0564 ⎟ ⎟ ⎜ ⎜ 0.0575 ⎟ ⎟ ⎜ ⎟ ⎜ ⎜ 0.0516 ⎟ ⎟ ⎜ ⎜ 0.0445 ⎟ ⎟ ⎜ ⎝ 0.0460 ⎠ 0.1803

(6)

From the ranking matrix B(V9 ) is the highest rank vaccine and 2nd ranking vaccine is M(V2 ) vaccine. In hot weather countries P(V1 ) vaccine storage is impossible and the cost is higher than other vaccines. But the efficiency of the vaccine is high in P(V1 ), M(V2 ), and B(V9 ). Table 5 shows the M(V2 ) is the highest priority and 2nd priority in the matrix is the P(V1 ) vaccine. The B(V9 ) vaccine price in the priority is high because it is cost effective other than the vaccine it is shown in Table 6. In Table 7 shows the storage temperature of the vaccine hot weather countries like India B(V9 ) vaccine more flexible so it is the highest priority. B(V9 ), P(V1 ) and M(V2 ) vaccine’s priority with respect to the variant ranking is 1st and 2nd position respectively shows in Table 8. These three vaccines effectively work 4 and 3 different types of mutant variants. All vaccines are good and they are worked successfully.

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4 Conclusion All the vaccines are given protection against the SARS-CoV-2 infection. The vaccines are created antibodies to fight against the SARS-CoV-2. All the vaccines are beneficial but sometime when vaccine shot will be taken adverse some minor effects like pain (joint and mussel), swelling, fever, itching, headache, vomiting. But sometimes major symptoms like blood clotting, DVT (deep vein thrombosis). In India, government has started vaccination for common people from 1st March 2021. In the end, we have a greater hope for ending this pandemic using this vaccination procedure.

References 1. Meo, S.A., Alhowikan, A.M., Al-Khlaiwi, T., Meo, I.M., Halepoto, D.M., Iqbal, M., Usmani, A.M., Hajjar, W., Ahmed, N.: Novel coronavirus 2019-nCoV: prevalence, biological and clinical characteristics comparison with SARS-CoV and MERS-CoV. Eur. Rev. Med. Pharmacol. Sci. 24, 2012–2019 (2020) 2. World Health Organization (WHO): Coronavirus disease (COVID-19) outbreak situation. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 25 Dec 2020 3. https://www.biospace.com/article/comparing-covid-19-vaccines-pfizer-biontech-moderna-ast razeneca-oxford-j-and-j-russia-s-sputnik-v/ 4. Meo, S.A., Abukhalaf, A.A., Alomar, A.A., Sattar, K., Klonoff, D.C.: COVID-19 pandemic: impact of quarantine on medical students’ mental wellbeing and learning behaviors. Pak. J. Med. Sci. 36, S43–S48 (2020) 5. US Food and Drug Administration: Pfizer-BioNTech COVID-19 vaccine. https://www.fda. gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/pfizer-bio ntech-covid-19-vaccine. Accessed 24 Dec 2020 6. US Food and Drug Ad. https://www.fda.gov/media/144412/download. Accessed 24 Dec 2020 7. US Food and Drug Ad.: Moderna COVID-19 vaccine. https://www.fda.gov/emergencypreparedness-and-response/coronavirus-disease-2019-covid-19/moderna-covid-19-vaccine. Accessed 24 Dec 2020 8. AZD1222 US Phase III primary analysis confirms safety and efficacy. https://www.astraz eneca.com/content/astraz/media-centre/press-releases/2021/azd1222-us-phase-iii-primaryanalysis-confirms-safety-and-efficacy.html 9. https://www.jnj.com/johnson-johnson-covid-19-vaccine-authorized-by-u-s-fda-for-emerge ncy-usefirst-single-shot-vaccine-in-fight-against-global-pandemic 10. Kaur, S.P., Gupta, V.: COVID-19 vaccine: a comprehensive status report. Virus Res. 288 (2020) 11. Anon, 2020n. Sinovac gets regulatory approval to assess Covid-19 vaccine. https://www.clinic altrialsarena.com. [Online] April 15. https://www.clinicaltrialsarena.com/news/sinovac-covid19-vaccine-trial-approval/ 12. https://www.pharmaceutical-technology.com/news/bharat-biotech-vaccine-efficacy-2/ 13. Mukherjee, P., Pattnaik, P.K., Swain, T.: The criteria for the cluster selection for single hop and multi-hop based sensor-cloud environment. Int. J. Knowl.-Based Intell. Eng. Syst. (IOS Publication) 23(1), 33–40 (2019)

RFID Based Patient Billing Automation Using Internet of Things (IoT) Suneeta Mohanty, Pranav Shekhar, Siddhartha Sinha, Arnab Poddar, Gevendra Sahu, and Ayushi Dash

Abstract In this information era, we cannot deny the dominance of information technology which has taken a major leap over the past decade, from the advent of silicon based processors to the rise of ground breaking algorithms, the way we look at complex problems with such ease has revolutionized solutions for this age, one such technology is the internet—the way it reaches the global population abridging distance for communication and work in such catastrophic times where maintaining a social distance is mandatory is remarkable. The significant thing to notice is that all kinds of remote devices are also being connected to the internet these days which aim to ease the living of mankind in automation, this domain is also known as the Internet of Things (IoT). The major objective of this work is to automate the billing process for patients in hospitals using RFID based scanners for better transparency and hassle free fast discharge of a patient so the bed can be provided to the other patient in need urgently in such an emergency like the pandemic where procuring a bed fast can save a life. Keywords Internet of Things (IoT) · RFID · Barcode · Hospital management · Hospital automation · Patient bill

S. Mohanty (B) · P. Shekhar · S. Sinha · A. Poddar · G. Sahu · A. Dash School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India e-mail: [email protected] P. Shekhar e-mail: [email protected] S. Sinha e-mail: [email protected] A. Poddar e-mail: [email protected] G. Sahu e-mail: [email protected] A. Dash e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_15

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1 Introduction We are living in such an age where right to information is fundamental for all living beings on this planet, we require transparency and fair transactions in all day to day scenarios we go through in our lives but this is not respected by a group of people who want to gain a major profit with such unfair and illegal information over our transactions. This is a common site at hospitals where a person puts everything at stake to save the life of his/her/their kin but this helplessness is often taken as an opportunity to gain profit over transactions by the hospital staff. We cannot deny the fact that this issue is real in existence in a country like ours. They often provide us with extra bills and charges in the name of legal services which are unjust and not fair. The other problem recognized in this solution is delay in discharge of a patient when he does not require the bed anymore but still has to face obligations due to the hassle in the billing system at the hospital. This often leads to a life and death situation for some other patient who is waiting for a bed to be discharged and given to him/her/them as quickly as possible so the treatment can start without further delay. This situation was a major problem identified in the second wave of COVID-19 in Indian hospitals where the system toppled due to lack of beds. We aim to provide a hassle free automated billing solution for hospitals based on RFID scanners with tags attached to a patient’s bed where no of barcode copies to be used will be equal to number of billing areas which will be scanned and immediately reflected on a patient’s bill and the everyday total will be sent to insurance companies to check the eligibility for claiming it. The system will fasten the process of patient discharge which will be crucial in vacating a bed and aiding others quickly. The system will also prevent black marketing of medicines as each medicine coming out of pharmacy will be tagged to each patient.

2 Related Work Nishitha et al. [1] implemented a Billing System which is automated and based on IOT, Uses the Barcode Scanner by Android Device and also Monitor the Unregistered Barcodes by RFID. The System is implemented as an automatic billing system for scanning of theft and provides ease in data analyzing and management purposes. Sharmila et al. [2] proposed a solution featuring a RFID equipped Shopping trolley for automated bill generation based on RFID system and uses cost and power efficient wireless communication ZigBee which provides assistance for the visually impaired. Similar concept of RFID equipped shopping trolley was suggested by Shahnoor [3], Saibannavar and Naik [4], Hanooja et al. [5], and Shankar et al. [6] in their respective works but with differences in hardware implementation and software dependencies but have similar frameworks. Sinha et al. [7] proposed a model implementing an Automatic Billing System that integrated an RFID system where the RFID tags are associated with the products. The

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RFID system and the Raspberry Pi are interconnected and can communicate using on device Wi-Fi with Amazon RDS MySQL instance where the database along with the prices of the products of the retail store is stored. Lambay et al. [8] discussed the technologies for the betterment and increasing the time efficiency for physical shopping by modifying existing models with Barcode Scanners, Database, Android Application and Wi-Fi module for communication. Sahare et al. [9] proposed an insight on automatic bill generating shopping system power by IoT devices such as using Raspberry Pi for wireless communication with server, web application for displaying the bills and RFID Tags attached to the items for Identification [5]. Polavarapu et al. [10] proposed an automatic tollgate collection system based on RFID [6]. From the RFID module, an interrupt sign is dispatched to Aurdino whenever The RFID tag which is attached to the vehicle receives a scan. The GSM module. LED and the motor driver receive a signal from the Microcontroller. The gate will lift and the vehicle is allowed to pass through the toll gate only when the LED turns from red to green after receiving the signal from the Microcontroller [8]. Radhika and Tajkiran [11] proposed a Smart and automated Billing System using RFID scanners attached with each Trolley, RFID tags on each product, and the final bill is displayed on the screen [9]. RFID is Preferred over the barcode in this system. Haddara and Staaby [12] discussed and reviewed the applications of RFID in the healthcare system for patient safety The RFID tags, attached with the patient as a hand band is scanned for the identification of the patients. The tracking and monitoring is also done with RFID. Smith-Ditizio and Smith [13] discussed RFID technologies to increase efficiency in the overall performance measurements and Issues in Healthcare results improving patients safety and flow. Its focus is on the process bottlenecks that delay discharge time and lead to low quality of service. Javaid and Khan [14] proposed solutions based on IoT systems in the medical field for finding the cause of disease, proper medical record-keeping, sampling, and integration of devices [14]. Vallabhuni et al. [15] proposed a Shopping trolley system with an Automated payment system, interfaced with RFID for Human Assistance includes an RFID reader controlled by Arduino [16]. Ved and Ghewari [16] proposed a Time-efficient RFID Based Shop Billing Machine using Raspberry Pi. In the system, the Bill will be generated by just putting the RFID card on the reader which is interfaced with Raspberry Pi.

3 Proposed Model To solve the problem of slow billing process and potential black-marketing of drugs, giving a RFID tag for the patient and all items and medicines which will be unique for every patient and also for individual items of the same category. For example if there are 1000 packs of the same medicine then each of those medicines will have

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a unique tag. This will help patients and administration to track and add medical facilities, pharmaceuticals, and consumables to patient’s bills. Using RFID scanner scan barcodes of every single medicine and medical consumable like gloves, PPE kit etc. Once the product is scanned then that product is removed from inventory and added to the patient’s bill. In detail, the average hospital setup, the medicines and medical consumables are managed and provided by the in campus pharmacies. These pharmacies maintain an inventory of all the medical stock. For example there can be 1000 leaflets of Drug A, 700 vials of Drug B, and 900 bottles of Drug C. They hold the information of how many of what drug is in stock but they do not identify each smallest unit of each and every drug. Similarly a patient admitted in hospital has a patient ID and the bill is managed with the help of that patient ID. From an administrative point of view it is a slow process as pharmacists manually enter medicines and consumables to patient’s bills by selecting each item from inventory and adding it to the bill. Nowadays people are generally insured either through private companies or Government policies like Ayushman Yojna. The clearing of bills through insurance companies takes time especially in case of Government provided insurance. In this pandemic we learned that every extra minute a bed stays occupied another needful patient could have been helped with hospital resources. Even in a general scenario, speeding up the billing process will bring positive changes in hospital administration. Automating this sending of daily bill summaries to insurance companies can speed up the billing process. From a patient’s point of view there is less transparency and can easily be conned as for example, a patient is given certain medicine which is then provided by the pharmacy. Sometimes what hospitals do is over prescribe the dose and the extra doses or even the whole medicine is sent back to the pharmacy and the patient gets charged for it. Same can be done to other patients at the same time, the hospitals go away with it as they show that returning drugs as replenishing the stock, they can simply pay off the taxes and have a clean working life. To even evade the taxes they can simply black market it outside which was clearly seen during the Covid-19 pandemic involving a life-saving drug. A. Stage 1: Scanning RFID scanning involves an active component which is the scanner, it uses electromagnetic fields to detect and read the passive component which is the RFID tag. It helps us to automatically identify and track tags that are attached to objects. Point the scanner in the direction of the product, no need for physical contact nor aligning the tag inline of the scanner. Figure 1 shows a hand-held RFID scanner sending radio waves to nearby RFID tag to read the information stored. B. Stage 2: Remove Item Once the medical item is scanned then that product is removed from stock inventory preventing fraudulent actions like adding a medicine to a patient’s bill but not giving it to the patient and the same process repeated for multiple patients. This insures clean administration and stops black marketing of drugs.

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Fig. 1 Scanning RFID tags

C. Stage 3: Append Item Item is added to the bill at the same instance it is removed from the inventory. D. Stage 4: Update Bill After adding all the items in the bill finally that bill is updated and linked to patient’s RFID tag which makes it impossible to resell that item again. E. Stage 5: Update Inventory Once the bill is updated then the inventory also gets automatically updated and all those items removed from available stock are now added to sold stock.

4 User Hardware Module Hardware components required for the implementation of proposed solution are as follows.

4.1 RFID Scanner RFID Scanner is a wireless device which acts as the active component i.e., it uses battery power for its functioning. It emits electromagnetic fields in form of Radio waves which hits the passive component which is the RFID tag, it automatically detects and reads the tag. RFID scanners and be used in different ways according to convenience such as continuously emitting scanners which as used in malls, etc., for high security and trigger operated scanner which is used just for scanning purposes. Figure 2 is an example of Hand-held RFID scanner which operates on battery power and sends Radio waves only when triggered. Its usability can find place in any store, market, pharmacy, etc., where there is need for tracking of goods and maintain an inventory.

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Fig. 2 RFID scanner

4.2 RFID Tags RFID Tags can come in both types, active and passive component. The active tag require external power source for its functioning. It is used in tracking operations as well as holds information of object to which it is attached. The passive tag does not require power source and works only in close proximity of a RFID scanner. It only holds information of the object it is attached to. RFID tags are smart barcodes that do not require physical touch or proper orientation to be scanned. RFID tags basically hold every information regarding the product it is on like price, date of manufacturing, serial no./lot no., and other information that a manufacturer chooses to feed into it. These are very cheap components; a passive tag’s cost can be as low as Rs. 0.15/piece or $0.002/piece. This can easily be used on such medical goods and drugs that are themselves very cheap and their cost won’t be affected severely so as to burden the consumer. Figure 3 shows a roll of passive RFID tags, each roll contains hundreds of individual RFID tags. Fig. 3 RFID tags

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5 Computational and Storage Module This model requires a database to store data and run query commands for handling the inventory. There are three available solutions depending on the type of end-user.

5.1 Local Storage over Distributed Computer System This approach is suitable of very small scale hospitals, nursing homes, clinics and pharmacies. It can be implemented on existing hardware system. The responsibility of backup, recovery and security will the sole responsibility of the administration.

5.2 Private Data Centers This approach is suitable for large hospitals and chain of hospitals (Chain of hospitals refers to institutions which are under administration of one group and own 100% infrastructure, equipments, and solely controls the staff). However costly, but data privacy and security is better. Also backup and recovery will be the sole responsibility of administration.

5.3 Cloud Resources This approach is suitable for franchise of hospitals (Franchise of hospitals refers to those institutions which is jointly operated by an individual and group. They lease their infrastructure and equipments to the group for them to manage staff and run the institution). Using cloud resources is cost effective and the administration can secure privacy and security of data and have no interference from the leaser. Backup and recovery will the responsibility of the Cloud service Provider.

6 Software Requirement Software compliant with RFID technology and can also run query language is required to maintain the inventory in the database. The market has numerous such softwares which can be used. Few popular softwares are below.

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Fig. 4 Snapshot of Tally

6.1 Tally Tally software is well-known software for generating bill and maintain inventory. It is compliant to RFID technology. It is already being used in almost every pharmacy. Figure 4 shows the snapshot of the Tally software currently being used to generate bill of a pharmacy.

6.2 IntelliTrack IntelliTrack is a popular cloud hosted as well as on-premise software compliant with RFID technology. Figure 5 shows the IntelliTrack snapshot of inventory management. There are various other software which are Cloud based, Web based or On-premise installation. Hospitals and pharmacies can choose any software which fits in their requirement and cost bracket.

7 System Implementation The system setup goes like this; first patients are given RFID tags just the way they used to get their name tags before. Figure 6 shows a patient equipped with hospital nametag as well as RFID code. This patient RFID contains Name, Age, Sex, ID, Doctor undertaking care, Department, etc. We use this Patient Tag to link the bill to the patient ensuring no item can be charged to the patient if it has been not. Also when there is quick discharge then

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Fig. 5 Snapshot of IntelliTrack

Fig. 6 Patient RFID tag

the patient needs not to wait any extra minute due to the time taken for generating the bill, it can be scanned at the bill desk and immediately the bill will be generated. In the Pharmacy, the drugs and medical utilities are also given RFID tags as shown in Fig. 7 which can only be sent out of the pharmacy when it undergoes a scan. All these are then connected via a Cloud database running SQL Query. Pharmacy can only access the inventory whereas the whole billing is accessed via Central Systems. Once a scan is done that particular item is removed from the Pharmacy inventory and added to the patient’s bill and shifted to the Sold database under the access of the central system. This Cloud-based approach is especially beneficial for chains of hospitals and helps put them under better Administration. Figure 8 demonstrates the overall System Implementation where mounted and hand-held RFID scanners scan the RFID tag on medical supplies and the patient nametag to then feed it into the software and store it in cloud database.

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Fig. 7 Medical consumable RFID tag

Fig. 8 System implementation

8 Results and Discussion The major takeaway which we got from this work is how to automate the billing process for patients in hospitals using RFID based scanners for better transparency and hassle-free fast discharge of a patient so the beds can be provided to the other patients in need, especially in such an emergency like the pandemic where procuring a bed faster can save a life. The patient’s RFID tag will contain Name, Age, Sex, ID, Doctor undertaking care, Department, etc. We will use this Patient Tag to link the bill to the patient ensuring no item can be charged to the patient if it has been not

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consumed. Also when there is a quick discharge then the other patients need not wait any extra minute due to the time is taken for generating the bill, it can be scanned at the bill desk and immediately the bill will be generated This system will fasten the process of patient discharge which will be crucial in vacating a bed and aiding others quickly, The system will also prevent black marketing of medicines as each medicine coming out of pharmacy will be tagged to each patient. Thus the patients and their families will get an account of every penny spent on the medical facilities of the patient. This will increase transparency among people and will also help the hospitals to gain the trust of the common people in such adverse situations like the pandemic where the hospital’s credibility is questioned quite often.

9 Conclusion and Future Work The constant questioning of the Indian healthcare system by the common man in this pandemic is a huge threat to our nation. Our healthcare facilities have been tossed upside down by this pandemic. Thus new changes regarding our healthcare are inevitable. Beds shortage, unfair means of accounting for admitted patients are some of the many causes that have caused the common man to lose trust in the healthcare system of India. Our system will help the patients and their families to know the exact cost of the medicines. It will help in increasing the transparency between the patient’s families and the hospitals and also will reduce the chances of black marketing of medicines. In the future we hope that the use of RFID scanners on patients should be made mandatory by all the hospitals to ease the process of patient discharge in order to make space for more critical patients in the queue. If this system is implemented properly the problem of shortage of beds will be eradicated completely thus making space for the more critical patients to be treated first and getting most of the healthcare facility.

References 1. Nishitha, R., Naik, S.S., Raksha, V., Kulsum, U.: IoT based automatic billing system using barcode scanner by android device and monitoring unregistered barcode by RFID. In: 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 15–20. IEEE (2019) 2. Sharmila, G., et al.: RFID based smart-cart system with automated billing and assistance for visually impaired. Mater. Today: Proc. (2021) 3. Shahnoor, S.F.: Smart cart for automatic billing with integrated RFID system. Turk. J. Comput. Math. Educ. (TURCOMAT) 12(12), 2487–2493 (2021) 4. Saibannavar, D., Naik, Y.: RFID based automatic shopping cart. J. Impact Factor 3, 26 (2018)

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5. Hanooja, T., Raji, C.G., Sreelekha, M., Koniyath, J., Ameen, V.M., Noufal, M.M.: Human friendly smart trolley with automatic billing system. In: 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1614–1619. IEEE (2020) 6. Shankar, S.K., Balasubramani, S., Basha, S.A., Ahamed, S.A., Reddy, N.S.K.: Smart trolley for smart shopping with an advance billing system using IoT. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 390–394. IEEE (2021) 7. Sinha, D., Cottur, K., Bhat, K., Guruprasad, C.: Automated billing system using RFID and cloud. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), vol. 1, pp. 1–6. IEEE (2019) 8. Lambay, M.A., Shinde, A., Tiwari, A., Sharma, V.: Automated billing cart. Int. J. Comput. Sci. Trends Technol. (IJCST) 5 (2017) 9. Sahare, P.S., Gade, A., Rohankar, J.: A review on automated billing for smart shopping system using IOT. 6(1), 1–5 (2019). http://iieta.org/Journals/RCES 10. Polavarapu, S.C., Umamaheswari, K., Hari, N.S.: RFID-based automatic tollgate collection. Int. J. Eng. Technol. (UAE) 7(2.1), 1–5 (2018) 11. Radhika, B., Tajkiran, V.: Smart billing system for shopping automation using IoT 12. Haddara, M., Staaby, A.: RFID applications and adoptions in healthcare: a review on patient safety. Procedia Comput. Sci. 138, 80–88 (2018) 13. Smith-Ditizio, A.A., Smith, A.D.: Radio frequency identification technologies and issues in healthcare. In: Encyclopedia of Information Science and Technology, 4th edn., pp. 5918–5929. IGI Global (2018) 14. Javaid, M., Khan, I.H.: Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 pandemic. J. Oral Biol. Craniofacial Res. 11(2), 209–214 (2021) 15. Vallabhuni, R.R., Lakshmanachari, S., Avanthi, G., Vijay, V.: Smart cart shopping system with an RFID interface for human assistance. In: 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 165–169. IEEE (2020) 16. Ved, M.D.D., Ghewari, P.B.: RFID based shop billing machine using Raspberry Pi. Dimensions 40, 60 (2019)

Correction to: Resource Management Challenges in IoT Based Healthcare System Roshni Pradhan, Amiya Kumar Dash, and Biswajit Jena

Correction to: Chapter “Resource Management Challenges in IoT Based Healthcare System” in: P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_4 In chapter 4 “Resource Management Challenges in IoT Based Healthcare System”, affiliation of co-author, Biswajit Jena’s mentioned as “Indian Institute of Information Technology Bhubaneswar, Bhubaneswar, India” instead of “International Institute of Information Technology (IIIT), Bhubaneswar, India”. This has now been corrected.

The updated version of this chapter can be found at https://doi.org/10.1007/978-981-16-5304-9_4

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9_16

C1

Author Index

A Aastha, 133 Acharya, Biswaranjan, 113

D Dash, Amiya Kumar, 31 Dash, Ayushi, 207 Das, Puja, 113

H Hol, Ana, 1

J Jamader, Asik Rahaman, 113 Jena, Biswajit, 31

K Koner, Debjit, 43 Kwon, Hye-jin, 191

L Lee, Jinseo, 65 Lee, Tae Hoon, 13

M Maity, Ritu, 103 Mallick, Pradeep Kumar, 43 Min, Shinhong, 165 Mishra, Pragatika, 113 Mishra, Priya, 21

Mishra, Ruby, 103 Mishra, Sibanand, 199 Mishra, Sushruta, 133 Mohanty, Aisworya, 75 Mohanty, Satarupa, 1, 133 Mohanty, Suneeta, 1, 207 Moon, Weon-Hee, 181 Mukherjee, Proshikshya, 199

N Nayak, Deepak Kumar, 113 Nayak, Suvendu Chandan, 75

P Panigrahi, Chhabi Rani, 75 Parida, Sasmita, 75 Park, Su In, 65 Pati, Bibudhendu, 75 Pattnaik, Prasant Kumar, 1, 103, 199 Poddar, Arnab, 207 Pradhan, Roshni, 31

R Rath, Sudhir Kumar, 199

S Sahu, Gevendra, 207 Shekhar, Pranav, 207 Shin, Moon Sam, 65 Sinha, Siddhartha, 207 Swain, Brijesh Raj, 21 Swetapadma, Aleena, 21

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics: State of the Art, Intelligent Systems Reference Library 213, https://doi.org/10.1007/978-981-16-5304-9

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220 T Tripathy, Pratiksha, 43 V Vaidya, Ashlesha, 1

Author Index Y Yun, Soon-Young, 165, 181