Advanced Healthcare Systems: Empowering Physicians with IoT-Enabled Technologies
- Length: 384 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-02-09
- ISBN-10: 1119768861
- ISBN-13: 9781119768869
- Sales Rank: #7268066 (See Top 100 Books)
ADVANCED HEALTHCARE SYSTEMS
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.
Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface 1 Internet of Medical Things—State-of-the-Art 1.1 Introduction 1.2 Historical Evolution of IoT to IoMT 1.2.1 IoT and IoMT—Market Size 1.3 Smart Wearable Technology 1.3.1 Consumer Fitness Smart Wearables 1.3.2 Clinical-Grade Wearables 1.4 Smart Pills 1.5 Reduction of Hospital-Acquired Infections 1.5.1 Navigation Apps for Hospitals 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 2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 2.1 Introduction 2.1 Related Works 2.3 Architecture 2.3.1 Device Layer 2.3.2 Fog Layer 2.3.3 Cloud Layer 2.4 Issues and Challenges 2.5 Conclusion References 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 3.5.1 Decision Tree Algorithm 3.5.2 Support Vector Machines 3.5.3 Random Forest 3.5.4 Logistic Regression 3.5.5 Naïve Bayes 3.6 Conclusion References 4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 4.1 Introduction 4.1.1 Introduction to IoT 4.1.2 Introduction to Vulnerability, Attack, and Threat 4.2 IoT in Healthcare 4.2.1 Confidentiality 4.2.2 Integrity 4.2.3 Authorization 4.2.4 Availability 4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 4.4 Conclusion References 5 Methods of Lung Segmentation Based on CT Images 5.1 Introduction 5.2 Semi-Automated Algorithm for Lung Segmentation 5.2.1 Algorithm for Tracking to Lung Edge 5.2.2 Outlining the Region of Interest in CT Images 5.3 Automated Method for Lung Segmentation 5.3.1 Knowledge-Based Automatic Model for Segmentation 5.3.2 Automatic Method for Segmenting the Lung CT Image 5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 5.5 Conclusion References 6 Handling Unbalanced Data in Clinical Images 6.1 Introduction 6.2 Handling Imbalance Data 6.2.1 Cluster-Based Under-Sampling Technique 6.2.2 Bootstrap Aggregation (Bagging) 6.3 Conclusion References 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 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? 8.3.1 Home Automation 8.3.2 Smart Appliances 8.3.3 Healthcare 8.4 Devices in Home Automation 8.4.1 Automatic Lights Control 8.4.2 Automated Home Safety and Security 8.5 Smart Appliances 8.5.1 Smart Oven 8.5.2 Smart Assistant 8.5.3 Smart Washers and Dryers 8.5.4 Smart Coffee Machines 8.5.5 Smart Refrigerator 8.6 Healthcare 8.6.1 Smart Watches 8.6.2 Smart Thermometer 8.6.3 Smart Blood Pressure Monitor 8.6.4 Smart Glucose Monitors 8.6.5 Smart Insulin Pump 8.6.6 Smart Wearable Asthma Monitor 8.6.7 Assisted Vision Smart Glasses 8.6.8 Finger Reader 8.6.9 Braille Smart Watch 8.6.10 Smart Wand 8.6.11 Taptilo Braille Device 8.6.12 Smart Hearing Aid 8.6.13 E-Alarm 8.6.14 Spoon Feeding Robot 8.6.15 Automated Wheel Chair 8.7 Conclusion References 9 IoT-Based Health Monitoring and Tracking System for Soldiers 9.1 Introduction 9.2 Literature Survey 9.3 System Requirements 9.3.1 Software Requirement Specification 9.3.2 Functional Requirements 9.4 System Design 9.4.1 Features 9.4.2 Pin Control Block 9.4.3 UARTs 9.4.4 System Control 9.4.5 Real Monitor 9.4.6 Temperature Sensor 9.4.7 Power Supply 9.4.8 Regulator 9.4.9 LCD 9.4.10 Heart Rate Sensor 9.5 Implementation 9.5.1 Algorithm 9.5.2 Hardware Implementation 9.5.3 Software Implementation 9.6 Results and Discussions 9.6.1 Heart Rate 9.6.2 Temperature Sensor 9.6.3 Panic Button 9.6.4 GPS Receiver 9.7 Conclusion References 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 10.3.1 Structured Data 10.3.2 Semi-Structured Data 10.4 Data Analysis 10.4.1 Descriptive Analysis 10.4.2 Diagnostic Analysis 10.4.3 Predictive Analysis 10.4.4 Prescriptive Analysis 10.5 ML Methods Used in Healthcare 10.5.1 Supervised Learning Technique 10.5.2 Unsupervised Learning 10.5.3 Semi-Supervised Learning 10.5.4 Reinforcement Learning 10.6 Probability Distributions 10.6.1 Discrete Probability Distributions 10.7 Evaluation Metrics 10.7.1 Classification Accuracy 10.7.2 Confusion Matrix 10.7.3 Logarithmic Loss 10.7.4 Receiver Operating Characteristic Curve, or ROC Curve 10.7.5 Area Under Curve (AUC) 10.7.6 Precision 10.7.7 Recall 10.7.8 F1 Score 10.7.9 Mean Absolute Error 10.7.10 Mean Squared Error 10.7.11 Root Mean Squared Error 10.7.12 Root Mean Squared Logarithmic Error 10.7.13 R-Squared/Adjusted R-Squared 10.7.14 Adjusted R-Squared 10.8 Proposed Methodology 10.8.1 Neural Network 10.8.2 Triangular Membership Function 10.8.3 Data Collection 10.8.4 Secured Data Storage 10.8.5 Data Retrieval and Merging 10.8.6 Data Aggregation 10.8.7 Data Partition 10.8.8 Fuzzy Rules for Prediction of Heart Disease 10.8.9 Fuzzy Rules for Prediction of Diabetes 10.8.10 Disease Prediction With Severity and Diagnosis 10.9 Experimental Results 10.10 Conclusion References 11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 11.1 Introduction 11.2 Background Elements 11.2.1 Security Comparison Between Traditional and IoT Networks 11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 11.3.1 Security Protocols 11.3.2 Enabling Technologies 11.4 CloudIoT Health System Framework 11.4.1 Data Perception/Acquisition 11.4.2 Data Transmission/Communication 11.4.3 Cloud Storage and Warehouse 11.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 11.4.5 Design Considerations 11.5 Security Challenges and Vulnerabilities 11.5.1 Security Characteristics and Objectives 11.5.2 Security Vulnerabilities 11.6 Security Countermeasures and Considerations 11.6.1 Security Countermeasures 11.6.2 Security Considerations 11.7 Open Research Issues and Security Challenges 11.7.1 Security Architecture 11.7.2 Resource Constraints 11.7.3 Heterogeneous Data and Devices 11.7.4 Protocol Interoperability 11.7.5 Trust Management and Governance 11.7.6 Fault Tolerance 11.7.7 Next-Generation 5G Protocol 11.8 Discussion and Analysis 11.9 Conclusion References 12 A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications 12.1 Introduction Machine Learning 12.2 Importance of Machine Learning 12.2.1 ML vs. Classical Algorithms 12.2.2 Learning Supervised 12.2.3 Unsupervised Learning 12.2.4 Network for Neuralism 12.3 Procedure 12.3.1 Dataset and Seizure Identification 12.3.2 System 12.4 Feature Extraction 12.5 Experimental Methods 12.5.1 Stepwise Feature Optimization 12.5.2 Post-Classification Validation 12.5.3 Fusion of Classification 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 13 Use of Machine Learning in Healthcare 13.1 Introduction 13.2 Uses of Machine Learning in Pharma and Medicine 13.2.1 Distinguish Illnesses and Examination 13.2.2 Drug Discovery and Manufacturing 13.2.3 Scientific Imaging Analysis 13.2.4 Twisted Therapy 13.2.5 AI to Know-Based Social Change 13.2.6 Perception Wellness Realisms 13.2.7 Logical Preliminary and Exploration 13.2.8 Publicly Supported Perceptions Collection 13.2.9 Better Radiotherapy 13.2.10 Incidence Forecast 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 13.5.1 Variation in Care 13.5.2 Inappropriate Care 13.5.3 Prevents Care–Associated Injurious and Death for Carefrontation 13.5.4 The Fact That People Are Unable to do What They Know Works 13.5.5 A Waste 13.6 A Team-Based Care Approach Reduces Waste 13.7 Conclusion References 14 Methods of MRI Brain Tumor Segmentation 14.1 Introduction 14.2 Generative and Descriptive Models 14.2.1 Region-Based Segmentation 14.2.2 Generative Model With Weighted Aggregation 14.3 Conclusion References 15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model 15.1 Introduction 15.2 Data Set 15.2.1 Data Insights 15.3 Feature Engineering 15.4 Framework for Early Detection of Disease 15.4.1 Deep Neural Network 15.5 Result 15.6 Conclusion References 16 A Comprehensive Analysis on Masked Face Detection Algorithms 16.1 Introduction 16.2 Literature Review 16.3 Implementation Approach 16.3.1 Feature Extraction 16.3.2 Image Processing 16.3.3 Image Acquisition 16.3.4 Classification 16.3.5 MobileNetV2 16.3.6 Deep Learning Architecture 16.3.7 LeNet-5, AlexNet, and ResNet-50 16.3.8 Data Collection 16.3.9 Development of Model 16.3.10 Training of Model 16.3.11 Model Testing 16.4 Observation and Analysis 16.4.1 CNN Algorithm 16.4.2 SSDNETV2 Algorithm 16.4.3 SVM 16.5 Conclusion References 17 IoT-Based Automated Healthcare System 17.1 Introduction 17.1.1 Software-Defined Network 17.1.2 Network Function Virtualization 17.1.3 Sensor Used in IoT Devices 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 Index
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