This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists.
The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book.
IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generate - information such as temperature, pressure, humidity, air quality, vibration, and sound - that can be really helpful to rapid diagnosis.
Audience
This book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector.
Table of Contents
Preface xvii
1 Internet of Medical Things - State-of-the-Art 1
Kishor Joshi and Ruchi Mehrotra
1.1 Introduction 2
1.2 Historical Evolution of IoT to IoMT 2
1.2.1 IoT and IoMT - Market Size 4
1.3 Smart Wearable Technology 4
1.3.1 Consumer Fitness Smart Wearables 4
1.3.2 Clinical-Grade Wearables 5
1.4 Smart Pills 7
1.5 Reduction of Hospital-Acquired Infections 8
1.5.1 Navigation Apps for Hospitals 8
1.6 In-Home Segment 8
1.7 Community Segment 9
1.8 Telehealth and Remote Patient Monitoring 9
1.9 IoMT in Healthcare Logistics and Asset Management 12
1.10 IoMT Use in Monitoring During COVID-19 13
1.11 Conclusion 14
References 15
2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 21
Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma
2.1 Introduction 22
2.2 Related Works 23
2.3 Architecture 25
2.3.1 Device Layer 25
2.3.2 Fog Layer 26
2.3.3 Cloud Layer 26
2.4 Issues and Challenges 26
2.5 Conclusion 29
References 30
3 Study of Thyroid Disease Using Machine Learning 33
Shanu Verma, Rashmi Popli and Harish Kumar
3.1 Introduction 34
3.2 Related Works 34
3.3 Thyroid Functioning 35
3.4 Category of Thyroid Cancer 36
3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 37
3.5.1 Decision Tree Algorithm 38
3.5.2 Support Vector Machines 39
3.5.3 Random Forest 39
3.5.4 Logistic Regression 39
3.5.5 Naïve Bayes 40
3.6 Conclusion 41
References 41
4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 43
Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi
4.1 Introduction 44
4.1.1 Introduction to IoT 44
4.1.2 Introduction to Vulnerability, Attack, and Threat 45
4.2 IoT in Healthcare 46
4.2.1 Confidentiality 46
4.2.2 Integrity 46
4.2.3 Authorization 46
4.2.4 Availability 47
4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 48
4.4 Conclusion 54
References 54
5 Methods of Lung Segmentation Based on CT Images 59
Amit Verma and Thipendra P. Singh
5.1 Introduction 59
5.2 Semi-Automated Algorithm for Lung Segmentation 60
5.2.1 Algorithm for Tracking to Lung Edge 60
5.2.2 Outlining the Region of Interest in CT Images 62
5.2.2.1 Locating the Region of Interest 62
5.2.2.2 Seed Pixels and Searching Outline 62
5.3 Automated Method for Lung Segmentation 63
5.3.1 Knowledge-Based Automatic Model for Segmentation 63
5.3.2 Automatic Method for Segmenting the Lung CT Image 64
5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 64
5.5 Conclusion 65
References 65
6 Handling Unbalanced Data in Clinical Images 69
Amit Verma
6.1 Introduction 70
6.2 Handling Imbalance Data 71
6.2.1 Cluster-Based Under-Sampling Technique 72
6.2.2 Bootstrap Aggregation (Bagging) 75
6.3 Conclusion 76
References 76
7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 81
Ishita Banerjee and Madhumathy P.
7.1 Introduction 82
7.2 Literature Survey 84
7.3 Procedure 86
7.4 Results 93
7.5 Conclusion 97
References 97
8 Smart IoT Devices for the Elderly and People with Disabilities 101
K. N. D. Saile and Kolisetti Navatha
8.1 Introduction 101
8.2 Need for IoT Devices 102
8.3 Where Are the IoT Devices Used? 103
8.3.1 Home Automation 103
8.3.2 Smart Appliances 104
8.3.3 Healthcare 104
8.4 Devices in Home Automation 104
8.4.1 Automatic Lights Control 104
8.4.2 Automated Home Safety and Security 104
8.5 Smart Appliances 105
8.5.1 Smart Oven 105
8.5.2 Smart Assistant 105
8.5.3 Smart Washers and Dryers 106
8.5.4 Smart Coffee Machines 106
8.5.5 Smart Refrigerator 106
8.6 Healthcare 106
8.6.1 Smart Watches 107
8.6.2 Smart Thermometer 107
8.6.3 Smart Blood Pressure Monitor 107
8.6.4 Smart Glucose Monitors 107
8.6.5 Smart Insulin Pump 108
8.6.6 Smart Wearable Asthma Monitor 108
8.6.7 Assisted Vision Smart Glasses 109
8.6.8 Finger Reader 109
8.6.9 Braille Smart Watch 109
8.6.10 Smart Wand 109
8.6.11 Taptilo Braille Device 110
8.6.12 Smart Hearing Aid 110
8.6.13 E-Alarm 110
8.6.14 Spoon Feeding Robot 110
8.6.15 Automated Wheel Chair 110
8.7 Conclusion 112
References 112
9 IoT-Based Health Monitoring and Tracking System for Soldiers 115
Kavitha N. and Madhumathy P.
9.1 Introduction 116
9.2 Literature Survey 117
9.3 System Requirements 118
9.3.1 Software Requirement Specification 119
9.3.2 Functional Requirements 119
9.4 System Design 119
9.4.1 Features 121
9.4.1.1 On-Chip Flash Memory 122
9.4.1.2 On-Chip Static RAM 122
9.4.2 Pin Control Block 122
9.4.3 UARTs 123
9.4.3.1 Features 123
9.4.4 System Control 123
9.4.4.1 Crystal Oscillator 123
9.4.4.2 Phase-Locked Loop 124
9.4.4.3 Reset and Wake-Up Timer 124
9.4.4.4 Brown Out Detector 125
9.4.4.5 Code Security 125
9.4.4.6 External Interrupt Inputs 125
9.4.4.7 Memory Mapping Control 125
9.4.4.8 Power Control 126
9.4.5 Real Monitor 126
9.4.5.1 GPS Module 126
9.4.6 Temperature Sensor 127
9.4.7 Power Supply 128
9.4.8 Regulator 128
9.4.9 LCD 128
9.4.10 Heart Rate Sensor 129
9.5 Implementation 129
9.5.1 Algorithm 130
9.5.2 Hardware Implementation 130
9.5.3 Software Implementation 131
9.6 Results and Discussions 133
9.6.1 Heart Rate 133
9.6.2 Temperature Sensor 135
9.6.3 Panic Button 135
9.6.4 GPS Receiver 135
9.7 Conclusion 136
References 136
10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques 137
G. K. Kamalam and S. Anitha
10.1 Introduction 138
10.2 Literature Survey 139
10.3 Medical Data Classification 141
10.3.1 Structured Data 142
10.3.2 Semi-Structured Data 142
10.4 Data Analysis 142
10.4.1 Descriptive Analysis 142
10.4.2 Diagnostic Analysis 143
10.4.3 Predictive Analysis 143
10.4.4 Prescriptive Analysis 143
10.5 ML Methods Used in Healthcare 144
10.5.1 Supervised Learning Technique 144
10.5.2 Unsupervised Learning 145
10.5.3 Semi-Supervised Learning 145
10.5.4 Reinforcement Learning 145
10.6 Probability Distributions 145
10.6.1 Discrete Probability Distributions 146
10.6.1.1 Bernoulli Distribution 146
10.6.1.2 Uniform Distribution 147
10.6.1.3 Binomial Distribution 147
10.6.1.4 Normal Distribution 148
10.6.1.5 Poisson Distribution 148
10.6.1.6 Exponential Distribution 149
10.7 Evaluation Metrics 150
10.7.1 Classification Accuracy 150
10.7.2 Confusion Matrix 150
10.7.3 Logarithmic Loss 151
10.7.4 Receiver Operating Characteristic Curve, or ROC Curve 152
10.7.5 Area Under Curve (AUC) 152
10.7.6 Precision 153
10.7.7 Recall 153
10.7.8 F1 Score 153
10.7.9 Mean Absolute Error 154
10.7.10 Mean Squared Error 154
10.7.11 Root Mean Squared Error 155
10.7.12 Root Mean Squared Logarithmic Error 155
10.7.13 R-Squared/Adjusted R-Squared 156
10.7.14 Adjusted R-Squared 156
10.8 Proposed Methodology 156
10.8.1 Neural Network 158
10.8.2 Triangular Membership Function 158
10.8.3 Data Collection 159
10.8.4 Secured Data Storage 159
10.8.5 Data Retrieval and Merging 161
10.8.6 Data Aggregation 162
10.8.7 Data Partition 162
10.8.8 Fuzzy Rules for Prediction of Heart Disease 163
10.8.9 Fuzzy Rules for Prediction of Diabetes 164
10.8.10 Disease Prediction With Severity and Diagnosis 165
10.9 Experimental Results 166
10.10 Conclusion 169
References 169
11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 173
Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan
11.1 Introduction 174
11.2 Background Elements 180
11.2.1 Security Comparison Between Traditional and IoT Networks 185
11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 187
11.3.1 Security Protocols 187
11.3.2 Enabling Technologies 188
11.4 CloudIoT Health System Framework 191
11.4.1 Data Perception/Acquisition 192
11.4.2 Data Transmission/Communication 193
11.4.3 Cloud Storage and Warehouse 194
11.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 194
11.4.5 Design Considerations 197
11.5 Security Challenges and Vulnerabilities 199
11.5.1 Security Characteristics and Objectives 200
11.5.1.1 Confidentiality 202
11.5.1.2 Integrity 202
11.5.1.3 Availability 202
11.5.1.4 Identification and Authentication 202
11.5.1.5 Privacy 203
11.5.1.6 Light Weight Solutions 203
11.5.1.7 Heterogeneity 203
11.5.1.8 Policies 203
11.5.2 Security Vulnerabilities 203
11.5.2.1 IoT Threats and Vulnerabilities 205
11.5.2.2 Cloud-Based Threats 208
11.6 Security Countermeasures and Considerations 214
11.6.1 Security Countermeasures 214
11.6.1.1 Security Awareness and Survey 214
11.6.1.2 Security Architecture and Framework 215
11.6.1.3 Key Management 216
11.6.1.4 Authentication 217
11.6.1.5 Trust 218
11.6.1.6 Cryptography 219
11.6.1.7 Device Security 219
11.6.1.8 Identity Management 220
11.6.1.9 Risk-Based Security/Risk Assessment 220
11.6.1.10 Block Chain-Based Security 220
11.6.1.11 Automata-Based Security 220
11.6.2 Security Considerations 234
11.7 Open Research Issues and Security Challenges 237
11.7.1 Security Architecture 237
11.7.2 Resource Constraints 238
11.7.3 Heterogeneous Data and Devices 238
11.7.4 Protocol Interoperability 238
11.7.5 Trust Management and Governance 239
11.7.6 Fault Tolerance 239
11.7.7 Next-Generation 5G Protocol 240
11.8 Discussion and Analysis 240
11.9 Conclusion 241
References 242
12 A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications 255
V. Arulkumar, D. Mansoor Hussain, S. Sridhar and P. Vivekanandan
12.1 Introduction Machine Learning 256
12.2 Importance of Machine Learning 256
12.2.1 ML vs. Classical Algorithms 258
12.2.2 Learning Supervised 259
12.2.3 Unsupervised Learning 261
12.2.4 Network for Neuralism 263
12.2.4.1 Definition of the Neural Network 263
12.2.4.2 Neural Network Elements 263
12.3 Procedure 265
12.3.1 Dataset and Seizure Identification 265
12.3.2 System 265
12.4 Feature Extraction 266
12.5 Experimental Methods 266
12.5.1 Stepwise Feature Optimization 266
12.5.2 Post-Classification Validation 268
12.5.3 Fusion of Classification Methods 268
12.6 Experiments 269
12.7 Framework for EEG Signal Classification 269
12.8 Detection of the Preictal State 270
12.9 Determination of the Seizure Prediction Horizon 271
12.10 Dynamic Classification Over Time 272
12.11 Conclusion 273
References 273
13 Use of Machine Learning in Healthcare 275
V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi
13.1 Introduction 276
13.2 Uses of Machine Learning in Pharma and Medicine 276
13.2.1 Distinguish Illnesses and Examination 277
13.2.2 Drug Discovery and Manufacturing 277
13.2.3 Scientific Imaging Analysis 278
13.2.4 Twisted Therapy 278
13.2.5 AI to Know-Based Social Change 278
13.2.6 Perception Wellness Realisms 279
13.2.7 Logical Preliminary and Exploration 279
13.2.8 Publicly Supported Perceptions Collection 279
13.2.9 Better Radiotherapy 280
13.2.10 Incidence Forecast 280
13.3 The Ongoing Preferences of ML in Human Services 281
13.4 The Morals of the Use of Calculations in Medicinal Services 284
13.5 Opportunities in Healthcare Quality Improvement 288
13.5.1 Variation in Care 288
13.5.2 Inappropriate Care 289
13.5.3 Prevents Care-Associated Injurious and Death for Carefrontation 289
13.5.4 The Fact That People Are Unable to do What They Know Works 289
13.5.5 A Waste 290
13.6 A Team-Based Care Approach Reduces Waste 290
13.7 Conclusion 291
References 292
14 Methods of MRI Brain Tumor Segmentation 295
Amit Verma
14.1 Introduction 295
14.2 Generative and Descriptive Models 296
14.2.1 Region-Based Segmentation 300
14.2.2 Generative Model With Weighted Aggregation 300
14.3 Conclusion 302
References 303
15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network-Based Model 305
Varun Sapra and Luxmi Sapra
15.1 Introduction 306
15.2 Data Set 307
15.2.1 Data Insights 308
15.3 Feature Engineering 310
15.4 Framework for Early Detection of Disease 312
15.4.1 Deep Neural Network 313
15.5 Result 314
15.6 Conclusion 315
References 315
16 A Comprehensive Analysis on Masked Face Detection Algorithms 319
Pranjali Singh, Amitesh Garg and Amritpal Singh
16.1 Introduction 320
16.2 Literature Review 321
16.3 Implementation Approach 325
16.3.1 Feature Extraction 325
16.3.2 Image Processing 325
16.3.3 Image Acquisition 325
16.3.4 Classification 325
16.3.5 MobileNetV2 326
16.3.6 Deep Learning Architecture 326
16.3.7 LeNet-5, AlexNet, and ResNet-50 326
16.3.8 Data Collection 326
16.3.9 Development of Model 327
16.3.10 Training of Model 328
16.3.11 Model Testing 328
16.4 Observation and Analysis 328
16.4.1 CNN Algorithm 328
16.4.2 SSDNETV2 Algorithm 330
16.4.3 SVM 331
16.5 Conclusion 332
References 333
17 IoT-Based Automated Healthcare System 335
Darpan Anand and Aashish Kumar
17.1 Introduction 335
17.1.1 Software-Defined Network 336
17.1.2 Network Function Virtualization 337
17.1.3 Sensor Used in IoT Devices 338
17.2 SDN-Based IoT Framework 341
17.3 Literature Survey 343
17.4 Architecture of SDN-IoT for Healthcare System 344
17.5 Challenges 345
17.6 Conclusion 347
References 347
Index 351
