Advanced Healthcare Systems

1 Cover

2 Title Page

3 Copyright

4 Preface

5 1 Internet of Medical Things—State-of-the-Art 1.1 Introduction 1.2 Historical Evolution of IoT to IoMT 1.3 Smart Wearable Technology 1.4 Smart Pills 1.5 Reduction of Hospital-Acquired Infections 1.6 In-Home Segment 1.7 Community Segment 1.8 Telehealth and Remote Patient Monitoring 1.9 IoMT in Healthcare Logistics and Asset Management 1.10 IoMT Use in Monitoring During COVID-19 1.11 Conclusion References

6 2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 2.1 Introduction 2.2 Related Works 2.3 Architecture 2.4 Issues and Challenges 2.5 Conclusion References

7 3 Study of Thyroid Disease Using Machine Learning 3.1 Introduction 3.2 Related Works 3.3 Thyroid Functioning 3.4 Category of Thyroid Cancer 3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 3.6 Conclusion References

8 4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 4.1 Introduction 4.2 IoT in Healthcare 4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 4.4 Conclusion References

9 5 Methods of Lung Segmentation Based on CT Images 5.1 Introduction 5.2 Semi-Automated Algorithm for Lung Segmentation 5.3 Automated Method for Lung Segmentation 5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 5.5 Conclusion References

10 6 Handling Unbalanced Data in Clinical Images 6.1 Introduction 6.2 Handling Imbalance Data 6.3 Conclusion References

11 7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 7.1 Introduction 7.2 Literature Survey 7.3 Procedure 7.4 Results 7.5 Conclusion References

12 8 Smart IoT Devices for the Elderly and People with Disabilities 8.1 Introduction 8.2 Need for IoT Devices 8.3 Where Are the IoT Devices Used? 8.4 Devices in Home Automation 8.5 Smart Appliances 8.6 Healthcare 8.7 Conclusion References

13 9 IoT-Based Health Monitoring and Tracking System for Soldiers 9.1 Introduction 9.2 Literature Survey 9.3 System Requirements 9.4 System Design 9.5 Implementation 9.6 Results and Discussions 9.7 Conclusion References

14 10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques 10.1 Introduction 10.2 Literature Survey 10.3 Medical Data Classification 10.4 Data Analysis 10.5 ML Methods Used in Healthcare 10.6 Probability Distributions 10.7 Evaluation Metrics 10.8 Proposed Methodology 10.9 Experimental Results 10.10 Conclusion References

15 11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 11.1 Introduction 11.2 Background Elements 11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 11.4 CloudIoT Health System Framework 11.5 Security Challenges and Vulnerabilities 11.6 Security Countermeasures and Considerations 11.7 Open Research Issues and Security Challenges 11.8 Discussion and Analysis 11.9 Conclusion References

16 12 A Novel Usage of Artificial Intelligence and Internet of Things in RemoteBased Healthcare Applications 12.1 Introduction Machine Learning 12.2 Importance of Machine Learning 12.3 Procedure 12.4 Feature Extraction 12.5 Experimental Methods 12.6 Experiments 12.7 Framework for EEG Signal Classification 12.8 Detection of the Preictal State 12.9 Determination of the Seizure Prediction Horizon 12.10 Dynamic Classification Over Time 12.11 Conclusion References

17 13 Use of Machine Learning in Healthcare 13.1 Introduction 13.2 Uses of Machine Learning in Pharma and Medicine 13.3 The Ongoing Preferences of ML in Human Services 13.4 The Morals of the Use of Calculations in Medicinal Services 13.5 Opportunities in Healthcare Quality Improvement 13.6 A Team-Based Care Approach Reduces Waste 13.7 Conclusion References

18 14 Methods of MRI Brain Tumor Segmentation 14.1 Introduction 14.2 Generative and Descriptive Models 14.3 Conclusion References

19 15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model 15.1 Introduction 15.2 Data Set 15.3 Feature Engineering 15.4 Framework for Early Detection of Disease 15.5 Result 15.6 Conclusion References

20 16 A Comprehensive Analysis on Masked Face Detection Algorithms 16.1 Introduction 16.2 Literature Review 16.3 Implementation Approach 16.4 Observation and Analysis 16.5 Conclusion References

21 17 IoT-Based Automated Healthcare System 17.1 Introduction 17.2 SDN-Based IoT Framework 17.3 Literature Survey 17.4 Architecture of SDN-IoT for Healthcare System 17.5 Challenges 17.6 Conclusion References

22 Index

23 End User License Agreement

1 Chapter 2Figure 2.1 Healthcare using IoT.Figure 2.2 Three-layer architecture.

2 Chapter 3Figure 3.1 Analysis of thyroid.Figure 3.2 Categories of thyroid cancer.Figure 3.3 Machine learning life cycle model.Figure 3.4 Analysis of machine learning approach on thyroid.

3 Chapter 4Figure 4.1 Conceptual framework of IoT.Figure 4.2 Barriers of IoT (Source: world economic forum industrial internet sur...

4 Chapter 5Figure 5.1 Flow of step for semi-automated way of segmenting both the lungs in C...Figure 5.2 The positions of S and M.Figure 5.3 (a) Searching in four directions. (b) Region growing based on seed.Figure 5.4 Step for lung segmentation using CT images.

5 Chapter 6Figure 6.1 Typical example of imbalanced data.Figure 6.2 Typical example of balanced data.Figure 6.3 The process of bootstrap aggregation.

6 Chapter 7Figure 7.1 Block diagram of proposed system.Figure 7.2 Arduino 2560 board.Figure 7.3 Voice kit APR 33A3.Figure 7.4 Working circuit diagram of LM35.Figure 7.5 Working of heartbeat sensor.Figure 7.6 Respiration sensor.Figure 7.7 Channels for heath monitoring.Figure 7.8 Flow chart of health monitoring.Figure 7.9 Hardware circuit for hand gesture recognition.Figure 7.10 Gesture and commands accordingly.Figure 7.11 Field chart for body temperature.Figure 7.12 Field chart for heartbeat.Figure 7.13 Field chart for respiration rate.

7 Chapter 8Figure 8.1 Chart showing growth in the usage of IoT devices.Figure 8.2 Areas of IoT devices used.

8 Chapter 9Figure 9.1 Functional block diagram.Figure 9.2 Functional block diagram of power supply.Figure 9.3 Heartbeat sensor.Figure 9.4 Output for heart rate.Figure 9.5 Output for temperature sensor.Figure 9.6 Output for panic button.Figure 9.7 Output for GPS services.

9 Chapter 10Figure 10.1 Overall classification framework.Figure 10.2 Data collection.Figure 10.3 Secured data storage.Figure 10.4 Data retrieval and merging.Figure 10.5 Data aggregation.Figure 10.6 Data partition.Figure 10.7 Training phase.Figure 10.8 Testing phase.Figure 10.9 Collect data from UCI repository dataset.Figure 10.10 Secured data storage.Figure 10.11 Fuzzy temporal neural classification.Figure 10.12 Disease prediction.Figure 10.13 Parameters vs. percentage.Figure 10.14 Methods vs. computational time.

10 Chapter 11Figure 11.1 CloudIoT applications.Figure 11.2 Research methodology.Figure 11.3 IoT layered architecture.Figure 11.4 Interface services.Figure 11.5 Deployment services.Figure 11.6 CloudIoT health architecture.Figure 11.7 Conceptual healthcare scenario.Figure 11.8 CloudIoT security goals.Figure 11.9 List of CloudIoT threats.

11 Chapter 12Figure 12.1 Machine learning steps.Figure 12.2 Difference between algorithmic and machine learning solution.Figure 12.3 Algorithms for machine study types.Figure 12.4 Supervised learning method.Figure 12.5 Classification separates the data; regression fits the data.Figure 12.6 Unsupervised model.Figure 12.7 Unsupervised process.Figure 12.8 Clustering with three clusters.Figure 12.9 General block diagram of NN.Figure 12.10 Internal layers of neural network.Figure 12.11 Block diagram of the EEG classification.Figure 12.12 Decision fusion block diagram.