Internet of Healthcare Things
Wiley-Scrivener (Verlag)
978-1-119-79176-8 (ISBN)
The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book.
Audience
Researchers and industry engineers in computer science, artificial intelligence, healthcare sector, IT professionals, network administrators, cybersecurity experts.
Kavita Sharma, PhD is an associate professor in the Department of CSE at Galgotias College of Engineering and Technology, Greater Noida, India. She has 4 patents (2 Granted and 2 published), published 6 books and 50 research articles in international journals and conferences. Her area of interest includes information and cyber security, mobile computing, IoT security, data analytics and machine learning. Yogita Gigras, PhD is an assistant professor in the Department of CSE & IT, School of Engineering & Technology of The North Cap University, Haryana, India. She has published more than 30 research papers in peer-reviewed international journals and conferences and has more than 12 years of teaching experience at both post and undergraduate level. Vishnu Sharma, PhD is Head of Department and Professor in Computer Science and Engineering at Galgotias College of Engineering and Technology Greater Noida, UP, India. He has published more than 50 research papers in international and national journals and conferences as well as two books on mobile computing. He has more than 21 years of teaching experience in engineering institutes and universities. D. Jude Hemanth, PhD is at the Department of ECE, Karunya University, Coimbatore, India. He has authored more than 100 research papers in SCIE/Scopus indexed international journals conferences as well as authored 1 book and edited 11 others. Ramesh Chandra (Poonia), PhD is an associate professor in the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India. He has authored more than 65 research papers in SCIE/Scopus indexed international journals conferences as well as authored 6 books.
Preface xiii
Section 1: Security and Privacy Concern in IoHT 1
1 Data Security and Privacy Concern in the Healthcare System 3
Ahuja Sourav
1.1 Introduction 3
1.2 Privacy and Security Concerns on E-Health Data 6
1.3 Levels of Threat to Information in Healthcare Organizations 6
1.4 Security and Privacy Requirement 9
1.5 Security of Healthcare Data 11
1.5.1 Existing Solutions 11
1.5.2 Future Challenges in Security and Privacy in the Healthcare Sector 15
1.5.3 Future Work to be Done in Security and Privacy in the Healthcare Sector 16
1.6 Privacy-Preserving Methods in Data 18
1.7 Conclusion 22
References 23
2 Authentication and Authorization Mechanisms for Internet of Healthcare Things 27
Srinivasan Lakshmi Narasimhan
2.1 Introduction 28
2.2 Stakeholders in IoHT 29
2.3 IoHT Process Flow 31
2.4 Sources of Vulnerability 33
2.5 Security Features 34
2.6 Challenges to the Security Fabric 35
2.7 Security Techniques—User Authentication 36
2.8 Conclusions 37
References 38
3 Security and Privacy Issues Related to Big Data-Based Ubiquitous Healthcare Systems 41
Jaspreet Singh
3.1 Introduction 41
3.2 Big Data Privacy & Security Issues 42
3.3 Big Data Security Problem 43
3.3.1 Big Data Security Lifecycle 44
3.3.2 Threats & Attacks on Big Data 47
3.3.3 Current Technologies in Use 48
3.4 Privacy of Big Data in Healthcare 50
3.4.1 Data Protection Acts 50
3.4.1.1 HIPAA Compliance 50
3.4.1.2 HIPAA Five Rules 53
3.5 Privacy Conserving Methods in Big Data 56
3.6 Conclusion 60
References 61
Section 2: Application of Machine Learning, Blockchain and Fog Computing on IoHT 65
4 Machine Learning Aspects for Trustworthy Internet of Healthcare Things 67
Pradeep Bedi, S.B. Goyal, Jugnesh Kumar and Preetishree Patnaik
4.1 Introduction 68
4.2 Overview of Internet of Things 69
4.2.1 Application Area of IoT 72
4.2.1.1 Wearable Devices 73
4.2.1.2 Smart Home Applications 73
4.2.1.3 Healthcare IoT Applications 73
4.2.1.4 Smart Cities 73
4.2.1.5 Smart Agriculture 74
4.2.1.6 Industrial Internet of Things 74
4.3 Security Issues of IoT 74
4.3.1 Authentication 75
4.3.2 Integrity 75
4.3.3 Confidentiality 75
4.3.4 Non-Repudiation 75
4.3.5 Authorization 76
4.3.6 Availability 76
4.3.7 Forward Secrecy 76
4.3.8 Backward Secrecy 76
4.4 Internet of Healthcare Things (IoHT): Architecture and Challenges 76
4.4.1 IoHT Support 77
4.4.2 IoHT Architecture and Data Processing Stages 78
4.4.3 Benefits Associated With Healthcare Based on the IoT 80
4.4.4 Challenges Faced by IoHT 81
4.4.5 Needs in IoHT 81
4.5 Security Protocols in IoHT 82
4.5.1 Key Management 83
4.5.2 User/Device Authentication 83
4.5.3 Access Control/User Access Control 83
4.5.4 Intrusion Detection 83
4.6 Application of Machine Learning for Intrusion Detection in IoHT 84
4.7 Proposed Framework 86
4.8 Conclusion 90
References 90
5 Analyzing Recent Trends and Public Sentiment for Internet of Healthcare Things and Its Impact
on Future Health Crisis 95
Upendra Dwivedi
5.1 Introduction 96
5.2 Literature Review 97
5.3 Overview of the Internet of Healthcare Things 100
5.4 Performing Topic Modeling on IoHTs Dataset 104
5.5 Performing Sentiment Analysis on IoHTs Dataset 107
5.6 Conclusion and Future Scope 110
References 111
6 Rise of Telemedicine in Healthcare Systems Using Machine Learning: A Key Discussion 113
Shaweta Sachdeva and Aleem Ali
6.1 Introduction 114
6.2 Types of Machine Learning 115
6.3 Telemedicine Advantages 115
6.4 Telemedicine Disadvantages 116
6.5 Review of Literature 116
6.6 Fundamental Key Components Needed to Begin Telemedicine 118
6.6.1 Collaboration Instruments 118
6.6.2 Clinical Peripherals 119
6.6.3 Work Process 119
6.6.4 Cloud-Based Administrations 119
6.7 Types of Telemedicine 119
6.7.1 Store-and-Forward Method 119
6.7.1.1 Telecardiology 120
6.7.1.2 Teleradiology 121
6.7.1.3 Telepsychiatry 121
6.7.1.4 Telepharmacy 121
6.7.2 Remote Monitoring 123
6.7.3 Interactive Services 123
6.8 Benefits of Telemedicine 124
6.9 Application of Telemedicine Using Machine Learning 125
6.10 Innovation Infrastructure of Telemedicine 125
6.11 Utilization of Mobile Wireless Devices in Telemedicine 126
6.12 Conclusion 127
References 128
7 Trusted Communication in the Healthcare Sector Using Blockchain 131
Balasamy K.
7.1 Introduction 131
7.2 Overview of Blockchain 133
7.3 Medical IoT Concerns 134
7.3.1 Security Concerns 134
7.3.2 Privacy Concerns 135
7.3.3 Trust Concerns 135
7.4 Needs for Security in Medical IoT 135
7.5 Uses of Blockchain in Healthcare 137
7.6 Solutions for IoT Healthcare Cyber-Security 138
7.6.1 Architecture of the Smart Healthcare System 139
7.6.1.1 Data Perception Layer 139
7.6.1.2 Data Communication Layer 140
7.6.1.3 Data Storage Layer 140
7.6.1.4 Data Application Layer 140
7.7 Executions of Trusted Environment 140
7.7.1 Root of Trust Security Services 141
7.7.2 Chain of Trust Security Services 143
7.8 Patient Registration Using Medical IoT Devices 144
7.8.1 Encryption 145
7.8.2 Key Generation 146
7.8.3 Security by Isolation 146
7.8.4 Virtualization 146
7.9 Trusted Communications Using Blockchain 149
7.9.1 Record Creation Using IoT Gateways 150
7.9.2 Accessibility to Patient Medical History 151
7.9.3 Patient Enquiry With the Hospital Authority 151
7.9.4 Blockchain-Based IoT System Architecture 151
7.9.4.1 First Layer 151
7.9.4.2 Second Layer 152
7.9.4.3 Third Layer 152
7.10 Combined Workflows 152
7.10.1 Layer 1: The Gateway Collects IoT Data and Generates a New Record 152
7.10.2 Layer 2: Gateway/Authority Want to Access Patient’s Medical Record 153
7.10.3 Layer 3: Patient Visits and Interact With an Authority 153
7.11 Conclusions 154
References 154
8 Blockchain in Smart Healthcare Management 161
Jayant Barak, Harshwardhan Chaudhary, Rakshit Mangal, Aarti Goel and Deepak Kumar Sharma
8.1 Introduction 162
8.2 Healthcare Industry 163
8.2.1 Classification of Healthcare Services 163
8.2.2 Health Information Technology (HIT) 164
8.2.3 Issues and Challenges Faced by Major Stakeholders in the Healthcare Industry 165
8.2.3.1 The Patient 166
8.2.3.2 The Pharmaceutical Industry 166
8.2.3.3 The Healthcare Service Providers 166
8.2.3.4 The Government 167
8.2.3.5 Insurance Company 167
8.3 Blockchain Technology 168
8.3.1 Important Terms 168
8.3.2 Features of Blockchain 170
8.3.2.1 Decentralization 170
8.3.2.2 Immutability 170
8.3.2.3 Transparency 171
8.3.2.4 Smart Contracts 171
8.3.3 Workings of a Blockchain System 171
8.3.4 Applications of Blockchain 173
8.3.4.1 Financial Services 173
8.3.4.2 Healthcare 173
8.3.4.3 Supply Chain 173
8.3.4.4 Identity Management 173
8.3.4.5 Voting 173
8.3.5 Challenges and Drawbacks of Blockchain 174
8.4 Applications of Blockchain in Healthcare 176
8.4.1 Electronic Medical Records (EMR) and Electronic Health Records (EHR) 176
8.4.2 Management System 177
8.4.3 Remote Monitoring/IoMT 178
8.4.4 Insurance Industry 179
8.4.5 Drug Counterfeiting 180
8.4.6 Clinical Trials 182
8.4.7 Public Health Management 182
8.5 Challenges of Blockchain in Healthcare 183
8.6 Future Research Directions 184
8.7 Conclusion 185
References 186
Section 3: Case Studies of Healthcare 189
9 Organ Trafficking on the Dark Web—The Data Security and Privacy Concern in Healthcare Systems 191
Romil Rawat, Bhagwati Garg, Vinod Mahor, Shrikant Telang, Kiran Pachlasiya and Mukesh Chouhan
9.1 Introduction 192
9.2 Inclination for Cybersecurity Web Peril 194
9.3 Literature Review 197
9.4 Market Paucity or Organ Donors 199
9.5 Organ Harvesting and Transplant Tourism Revenue 203
9.6 Social Web Net Crimes 204
9.7 DW—Frontier of Illicit Human Harvesting 209
9.8 Organ Harvesting Apprehension 209
9.9 Result and Discussions 212
9.10 Conclusions 212
References 213
10 Deep Learning Techniques for Data Analysis Prediction in the Prevention of Heart Attacks 217
C.V. Aravinda, Meng Lin, Udaya Kumar, Reddy K.R. and G. Amar Prabhu
Abbreviations 218
10.1 Introduction 218
10.2 Literature Survey 219
10.3 Materials and Method 221
10.3.1 Cohort Study 222
10.4 Training Models 222
10.4.1 Artificial Neural Network (ANN) 222
10.4.2 K-Nearest Neighbor Classifier 224
10.4.3 Naïve Bayes Classifier 225
10.4.4 Decision Tree Classifier (DTC) 226
10.4.5 Random Forest Classifier (RFC) 226
10.4.6 Neural Network Implementation 226
10.5 Data Preparation 227
10.5.1 Multi-Layer Perceptron Neural Network (MLPNN) Algorithm and Prediction 227
10.6 Results Obtained 228
10.6.1 Accuracy 228
10.6.2 Data Analysis 228
10.7 Conclusion 236
References 236
11 Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT) 241
Monika Lamba, Geetika Munjal and Yogita Gigras
11.1 Introduction 242
11.2 Related Work 245
11.3 IoT and Disease 250
11.4 Research Materials and Methods 251
11.4.1 Dataset 251
11.4.2 Data Pre-Processing 252
11.4.3 Classification Algorithms 252
11.5 Experimental Outcomes 253
11.6 Conclusion 257
References 258
12 Perspective-Based Studies of Trust in IoHT and Machine Learning-Brain Cancer 265
Sweta Kumari, Akhilesh Kumar Sharma, Sandeep Chaurasia and Shamik Tiwari
12.1 Introduction 266
12.2 Literature Survey 267
12.3 Illustration of Brain Cancer 268
12.3.1 Brain Tumor 268
12.3.2 Types of Brain Tumors 269
12.3.3 Grades of Brain Tumors 270
12.3.4 Symptoms of Brain Tumors 271
12.4 Sleuthing and Classification of Brain Tumors 273
12.4.1 Sleuthing of Brain Tumors 273
12.4.2 Challenges During Classification of Brain Tumors 274
12.5 Survival Rate of Brain Tumors 274
12.6 Conclusion 278
References 279
Index 281
| Erscheinungsdatum | 15.03.2022 |
|---|---|
| Reihe/Serie | Machine Learning in Biomedical Science and Healthcare Informatics |
| Sprache | englisch |
| Maße | 10 x 10 mm |
| Gewicht | 454 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Medizin / Pharmazie ► Medizinische Fachgebiete | |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Medizintechnik | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-119-79176-6 / 1119791766 |
| ISBN-13 | 978-1-119-79176-8 / 9781119791768 |
| Zustand | Neuware |
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