IoT and ML for Information Management: A Smart Healthcare Perspective

This book discusses the roles of the Internet of Things (IoT) and machine learning (ML) in smart health care, including the integration of cloud computing with IoT and ML for managing healthcare data. It presents the fundamentals and many applications of IoT and ML in different areas of smart health care. It deliberates upon security and privacy issues, including trust concerns about smart healthcare systems based on IoT and ML algorithms. This book is concluded by discussing challenges and future work directions in smart health care using IoT and ML, and it serves as a reference resource for researchers and practitioners in academia and industry. Preface Contents Editor and Contributors Introduction to Internet of Things 1 Introduction 2 Fundamentals of IoT 2.1 Historical Development 2.2 Components 2.3 Characteristics 3 Advantages of IoT 4 IoT Architecture 5 Key Technologies of IoT 5.1 Hardware Platforms 5.2 Wireless Communication Technology 5.3 Cloud Solutions 5.4 Hardware and Software Technologies 6 Applications of IoT 6.1 Smart Cities 6.2 Medical and Healthcare 6.3 Smart Agriculture and Environment 6.4 Smart Home (SH) 6.5 Smart Manufacturing System (SMS) 6.6 Internet of Robotics Things (IoRT) 6.7 Oil and Gas Industry 6.8 Smart Retail 6.9 Industrial Internet of Things (IIoT) 6.10 Social Life and Entertainment 7 Challenges and Future Directions 7.1 Broad and Open Research Challenges 7.2 Ethical Considerations 7.3 Legal and Regulatory Issues 8 Conclusions References Introduction to Machine Learning 1 Introduction 2 History of ML 3 Importance of ML 3.1 SL 3.2 UL 3.3 RL 4 Design of ML Experiment 4.1 Model Complexity and Generalization 4.2 Selection of Dataset 4.3 Randomization and Cross-Validation 4.4 Performance Metrics 5 Major Issues in ML Algorithms 6 Applications of ML 7 Conclusions and Future Works References Internet of Things and Machine Learning for Smart Healthcare 1 Introduction 2 Internet of Health Things (IoHT) 2.1 Remote Monitoring 2.2 Wearables and Smartphones in Smart Healthcare 2.3 Security and Privacy of IoHT 3 Interoperability and Health Data Management 3.1 Interoperability 3.2 Blockchain in Smart Healthcare 4 Applications of IoT in Smart Healthcare 4.1 IoT for Doctors 4.2 IoT for Hospitals and Pharmaceutical Sector 4.3 IoT for Patients 5 Applications of ML in Smart Healthcare 5.1 Prediction and Diagnosis of Diseases 5.2 Medical Imaging 5.3 Drug Discovery 5.4 Health Record 5.5 Clinical Trial and Research 5.6 Federated Learning 5.7 Explainable Artificial Intelligence 6 Challenges and Future Directions 6.1 Challenges 6.2 Future Work 7 Conclusion References Machine Learning for Smart Healthcare Management Using IoT 1 Introduction 2 Architecture of Smart Healthcare Monitoring System 2.1 Client-Server Architecture 2.2 Cloud-Based Architecture 2.3 Edge Computing Architecture 2.4 IoT Architecture 2.5 Federated Architecture 2.6 Blockchain-Based Architecture 2.7 AI-Driven Architecture 2.8 Hybrid Architecture 3 ML-Based Smart Healthcare Monitoring Systems Using IoT 3.1 Integration of IoT and Healthcare 3.2 The Role of ML in Healthcare Data Analysis 3.3 ML-Based Smart Healthcare Monitoring Systems 4 ML-Based Remote Healthcare Monitoring Systems Using IoT 4.1 Foundations of Remote Healthcare Monitoring 4.2 IoT-Enabled Healthcare Devices and Data Acquisition 4.3 Integration of IoT and ML in Remote Healthcare Monitoring 4.4 Data Privacy and Security in Remote Healthcare Monitoring 5 ML-Based Telemedicine Systems Using IoT 5.1 Cloud-Hosted Smart Telemedicine Applications 5.2 On-Premises Predictive Telemedicine Solutions 5.3 Integrated Telemedicine Services 6 ML-Based Models for Medical Big Data Using IoT 6.1 Deep Learning for Accurate Healthcare Prediction and Recommendation 6.2 Handling Real-Time Healthcare Data with Reinforcement Learning 6.3 Federated Learning for Secured Healthcare Services 6.4 Explainable Healthcare with XAI 7 Discussion 7.1 Research Gaps 7.2 Future Directions 8 Conclusions References E-Healthcare Data Management Using Machine Learning and IoT 1 Introduction 2 Electronic Health Record 2.1 History of Health Records 2.2 The Use of EHRs to Improve Care 2.3 Benefits of EHR 2.4 Risks of EHR 3 IoT Sensor and Networks Used for E-Healthcare Data Management 3.1 Sensors and Networks 3.2 Advantages and Disadvantages of IoT Sensors 4 ML-Based E-Healthcare Data Management Techniques Using IoT 4.1 ML-Based IoT Management Techniques 4.2 Enhancing Clinical Trials Through Machine Learning and IoT: Optimizing Phases and Improving Efficiency 4.3 Existing Schemes 4.4 Basics of Blockchain and Challenges 5 ML-Based E-Healthcare Data Security Techniques Using IoT 5.1 Evolution of Healthcare 5.2 Taxonomy of Security in Privacy and Healthcare 5.3 Regulatory Perspective of Device-Driven Digital Health 5.4 Enhancing Healthcare Security Through Biometric Two-Factor Authentication 5.5 ML Algorithms in Biometric Recognition for Healthcare Security 5.6 ECG Biometric Recognition 5.7 Existing and Proposed Schemes 6 Future Work Directions 6.1 Technologies in the Healthcare Field that Could Make Headway 6.2 Developing a Blockchain-Based Platform for Secure and Decentralized Data Management 7 Conclusions References Machine Learning and IoT in Precision Healthcare 1 Introduction 2 Factors Affecting Precision Healthcare 2.1 Lifestyle Factors in Precision Healthcare 2.2 Biological Factors in Precision Healthcare 3 Precision Medicine 3.1 Primary Building Blocks of Precision Medicine 3.2 Personalized Healthcare Using Precision Medicine 3.3 Precision Medicine with Feedback Control 4 Machine Learning Techniques in Precision Healthcare 4.1 Prospects for Machine Learning in Precision Healthcare 4.2 Need for Machine Learning in Healthcare 4.3 Can Machine Learning Be the Ultimate Solution to Problems Faced in the Healthcare Sector? 4.4 Implementation of Machine Learning Algorithms 5 IoT Techniques in Precision Healthcare 5.1 IoT Devices in Healthcare 5.2 Remote Patient Analysis 5.3 Wearable Devices 5.4 Smart Implant 5.5 Telemedicine 6 Existing ML-Based Techniques for Precision Healthcare Using IoT 6.1 Natural Language Processing (NLP) and Robotics 6.2 Predictive Analytics in Precision Healthcare 7 Applications of AI in Precision Healthcare 7.1 Genomic Data Analysis 7.2 Forecasting Diseases 7.3 Virtual Health Assistants (VHA) 7.4 Medical Imaging and Diagnostics 8 Technical Challenges in the Implementation of ML and IoT in Precision Healthcare 8.1 Data Integration Challenges 8.2 Data Quality and Consistency Issues 8.3 Data Volume and Scalability Issues 8.4 Privacy and Security Concerns 9 Conclusions and Future Scopes References Machine Learning and IoT in Health 4.0 1 Introduction 2 Fundamentals of Health 4.0 3 Issues of Health 4.0 4 Significance of Big Data in Health 4.0 5 Management of Health 4.0 Using ML Algorithms and IoT 5.1 Patient Relationship Management 5.2 Securing Healthcare Data 5.3 Health Insurance Management 5.4 Drug Traceability 5.5 Prescription Management 6 Conclusions and Future Scopes References Utilizing Artificial Intelligence and IoT Technologies for Enhanced COVID-19 Diagnosis 1 Introduction 2 Literature Review 3 Background Studies 3.1 IoT Applications for COVID-19 3.2 Machine Learning Applications in COVID-19 4 Proposed Scheme 5 Performance Analysis 5.1 Experimental Environment 5.2 Details of Dataset 5.3 Results and Discussion 6 Conclusions and Future Work References Analysing e-Healthcare Data from Internet of Things Devices and Cloud Computing 1 Introduction 2 Preliminary Studies on Healthcare Integration in Edge/Fog/IoT Devices and Cloud Computing 3 Related Work 4 Performance Evaluation and Novelty of Proposed Work Under Different Healthcare Verticals 5 Conclusions and Future Work References Toward Smart Healthcare: Challenges and Opportunities in IoT and ML 1 Introduction 2 Exploring ML and IoT Applications 2.1 Practical Uses of IoT in Smart Healthcare 2.2 Practical Uses of ML in Smart Healthcare 2.3 Healthcare Applications Using ML and IoT 3 Research Challenges 3.1 IoT-Based Systems 3.2 ML-Based Systems 3.3 IoT- and ML-Based Systems 4 Future Work Directions 4.1 IoT-Based Systems 4.2 ML-Based Systems 4.3 IoT- and ML-Based Systems 5 Conclusions References