Wellness Management Powered by AI Technologies

Chapter 1 Exploring Functional Modules Using Co-Clustering of Protein Interaction Networks
	1.1 Introduction
	1.2 Related Works
	1.3 Basic Terminologies
		1.3.1 Scientific Terms Used
	1.4 Existing Methods
		1.4.1 Binary Co-Clustering Approaches
			1.4.1.1 Binary Inclusion-Maximal Algorithm
			1.4.1.2 xMotif Algorithm
	1.5 About Dataset
		1.5.1 Protein Interaction Networks
			1.5.1.1 STRING Repository
		1.5.2 Protein Complex Dataset
			1.5.2.1 CORUM Database
	1.6 Experimental Environment
		1.6.1 MapReduce Framework
	1.7 Validation Measures
		1.7.1 Match Score Measure
		1.7.2 Functional Coherence
	1.8 Biological Significances
	1.9 Proposed Co-Clustering Approach: MR-CoC
		1.9.1 SCoC for Non-Symmetric Matrix
			1.9.1.1 Toy Example: SCoCnsym
			1.9.1.2 Synthetic Dataset Description
			1.9.1.3 Experimental Analysis: SCoCnsym
		1.9.2 Randomized SCoC
			1.9.2.1 Synthetic Dataset Description
			1.9.2.2 Experimental Analysis: SCoCrand
		1.9.3 SCoC with MapReduce (MR-CoC)
			1.9.3.1 Synthetic Dataset Description
			1.9.3.2 Experimental Analysis: MR-CoC
	1.10 Functional Module Mining Using MR-CoC
	1.11 Conclusion
	Appendix
	References
Chapter 2 Natural Language Processing in Healthcare: Enhancing Wellbeing through a COVID-19 Case Study
	2.1 Introduction
	2.2 NLP Approaches
	2.3 NLP Pipeline for Smart Healthcare
		2.3.1 Preprocessing
		2.3.2 Feature Extraction
		2.3.3 Classification
		2.3.4 Model Interpretability
	2.4 Applications of NLP in Healthcare
		2.4.1 Clinical Records
		2.4.2 Information Extraction
		2.4.3 Decision Support
		2.4.4 Health Assistance
		2.4.5 Opinion Mining
	2.5 COVID Detection Using NLP
		2.5.1 Data Collection
		2.5.2 Preprocessing
		2.5.3 Feature Engineering
		2.5.4 Classification
		2.5.5 Ensemble Classification
	2.6 Results and Discussion
		2.6.1 Traditional Machine Learning
		2.6.2 Ensemble Machine Learning
	2.7 Conclusion
	References
Chapter 3 Artificial Intelligence Assisted Internet of Medical Things (AIoMTs) in Sustainable Healthcare Ecosystem
	3.1 Introduction
		3.1.1 Key Contributions of the Chapter
		3.1.2 Chapter Organization
	3.2 Medical Wearable Electronics
		3.2.1 Electronic Sensor Traits
		3.2.2 Disposable Health Sensors
		3.2.3 Ingestible Sensors
		3.2.4 Patch Sensors
		3.2.5 Connected Health Sensors
		3.2.6 Wearables
		3.2.7 Smart Clothing
		3.2.8 Implantable Sensors
	3.3 Electronic Signals in Sensors
		3.3.1 Gait Analysis
		3.3.2 Photoplethysmography
		3.3.3 Electromyography
		3.3.4 Auscultation
	3.4 Electronic Devices Challenges in the AIoMT
		3.4.1 Data Security Threats
		3.4.2 Data Interoperability
		3.4.3 Regulatory Challenges
		3.4.4 High Infrastructure Costs
		3.4.5 Standardization Challenges
		3.4.6 Cybersecurity
		3.4.7 Device Mobility
		3.4.8 Adoption Scale
		3.4.9 Advanced Analytics
		3.4.10 Trust Maintenance
		3.4.11 Data Security
		3.4.12 Licensing Challenge
	3.5 AIoMT Benefits
		3.5.1 Medical Diagnosis
		3.5.2 Medical Treatment
		3.5.3 Patie nt Empowerment
		3.5.4 Reduction in Medical Costs
		3.5.5 Reduction in Human Error
	3.6 AIoMTs Challenges
		3.6.1 Privacy Concerns
		3.6.2 Missteps and Errors
		3.6.3 Data Management and Power Issues
		3.6.4 Bias
	3.7 AIoMT Limitations
	3.8 Future Research Direction
	3.9 Conclusions and Future Scope
	References
Chapter 4 An Online Platform for Timely Access to Medical Care with the Help of Real-Time Data Analysis
	4.1 Introduction
		4.1.1 Research Questions
		4.1.2 Inspiration Drawn
		4.1.3 Limitations
		4.1.4 Importance of Machine Learning in this Research Work
	4.2 What Happened
	4.3 Literature Review
	4.4 Methodology
		4.4.1 Dataset Collection
		4.4.2 Data Preprocessing
		4.4.3 Model Building
		4.4.4 Clustering Algorithm
		4.4.5 A* Algorithm
	4.5 Hardware Component
		4.5.1 Blockchain in Health Care
	4.6 Conclusion
	4.7 Future Work
	References
Chapter 5 A Comprehensive Review of Cardiac Image Analysis for Precise Heart Disease Diagnosis Using Deep Learning Techniques
	5.1 Introduction and Major Contribution
	5.2 Literature Review
	5.3 Machine Learning Methods
	5.4 Proposed System
		5.4.1 Dataset
		5.4.2 Preprocessing
		5.4.3 Network Architecture
	5.5 Mathematical Model
	5.6 Data Preparation
	5.7 Model Training and Evaluation
	5.8 Results and Discussion
	5.9 Conclusion and Future Work
	References
Chapter 6 A Hybrid Machine Learning Model for an Efficient Detection of Liver Inflammation
	Abbreviations
	6.1 Introduction
		6.1.1 Novelty of Detection of NAFLD Using Conglomeration of Machine Learning Techniques
	6.2 Machine Learning for Liver Disease Prediction
		6.2.1 Data Collection and Pre-Processing
		6.2.2 Feature Selection
		6.2.3 Modeling with Algorithms
		6.2.4 Evaluating the Models
	6.3 Related Works
		6.3.1 Method
		6.3.2 Detecting Liver Inflammation with Random Forest Classifier
	6.4 Experimental Analysis
	6.5 Result Evaluation
	6.6 Conclusion
	6.7 Enhancement of PCA Over Other Dimensionality Reductions
	References
Chapter 7 Advancements in Parkinson’s Disease Diagnosis through Automated Speech Analysis
	7.1 Introduction
		7.1.1 Overview
		7.1.2 Traditional Diagnostic Methods
		7.1.3 Emergence of Automated Speech Analysis
		7.1.4 Major Contributions of the Work
	7.2 Speech Characteristics in Parkinson’s Disease
		7.2.1 Speech-Related Difficulties
		7.2.2 Specific Speech Features
	7.3 Technological Advances in Speech Analysis
		7.3.1 Digital Signal Processing
		7.3.2 Machine Learning and Artificial Intelligence
	7.4 Integration of Multimodal Data
		7.4.1 Complementary Modalities
		7.4.2 Improved Diagnostic Precision
	7.5 Related Works
	7.6 Building a Machine Learning (ML) Model
		7.6.1 Dataset Description
		7.6.2 Preprocessing
		7.6.3 Feature Extraction
		7.6.4 Classification
	7.7 Experimental Analysis and Performance Measures
		7.7.1 Evaluating Classifiers
		7.7.2 Tuning Hyperparameters
	7.8 Future Directions
		7.8.1 Advancements in Technology
		7.8.2 Personalized Medicine
	7.9 Challenges and Limitations
		7.9.1 Influencing Factors
		7.9.2 Ethical Considerations
		7.9.3 Standardization and Validation
	7.10 Conclusion and Implications
		7.10.1 Implications for Clinical Practice
	References
Chapter 8 Public Opinion Segmentation on COVID-19 Vaccination and Its Impact on Wellbeing
	8.1 Introduction
	8.2 Background and Related Work
	8.3 Machine Learning Techniques
		8.3.1 Logistic Regression
		8.3.2 Multinomial Naïve Bayes
		8.3.3 Support Vector Machine (SVM)
		8.3.4 Decision Trees
	8.4 Ensemble Machine Learning Algorithms
		8.4.1 Bagging
		8.4.2 AdaBoost
		8.4.3 Random Forest Classifier
		8.4.4 Stochastic Gradient Boosting
	8.5 Methodology
		8.5.1 Data Collection
		8.5.2 Data Preprocessing
		8.5.3 Feature Engineering
		8.5.4 Classification
	8.6 Results and Discussion
	8.7 Impact on Wellbeing
	8.8 Conclusion
	References
Chapter 9 Revolutionizing Healthcare with IoT in Cardiology
	9.1 Introduction
		9.1.1 Characteristics of IoT
		9.1.2 Healthcare
		9.1.3 Components of Healthcare
		9.1.4 The Role of IoT in Healthcare
			9.1.4.1 Remote Monitoring and Management
			9.1.4.2 Personalized Healthcare
			9.1.4.3 Enhancing Hospital Efficiency and Patient Experience
			9.1.4.4 Telemedicine and Remote Consultations
			9.1.4.5 Improving Emergency Responses
			9.1.4.6 Drug Management and Supply Chain Optimization
	9.2 Background
	9.3 Motivation
		9.3.1 Access to Healthcare
		9.3.2 Cost and Affordability
		9.3.3 Quality of Care
		9.3.4 Aging Population and Chronic Diseases
		9.3.5 Healthcare Infrastructure
		9.3.6 Healthcare Technology and Innovation
		9.3.7 Global Health Threats
		9.3.8 Mental Health
	9.4 Primary Diseases Globally
	9.5 IoT Revolutionizes Healthcare
	9.6 IoT Patient Monitoring Devices and Early Detection of Heart-Related Problems
	9.7 An IoT-Based Heart Disease Monitoring System
		9.7.1 Photoplethysmography
		9.7.2 Software Requirements
		9.7.3 Hardware Prerequisite
	9.8 Conclusions
	References
Chapter 10 Human Biological Analysis Through Fitness Watch Using Deep Learning Algorithm
	10.1 Introduction
	10.2 Literature Survey
	10.3 Methodology
	10.4 Results and Discussion
	10.5 Limitation of the Work
	10.6 Validation and Comparative Analysis
	10.7 Conclusion
	References
Chapter 11 Decoding Kidney Health: Effectiveness of Machine Learning Techniques in Diagnosis of Chronic Kidney Disease
	11.1 Introduction
	11.2 Methods
		11.2.1 Data and Features
		11.2.2 Preprocessing
	11.3 Methodology
		11.3.1 Logistic Regression
		11.3.2 Random Forest
		11.3.3 KNN
		11.3.4 Support Vector Machine (SVM)
		11.3.5 Decision Tree
		11.3.6 Adjusting Hyperparameters
		11.3.7 Boosting Algorithm
	11.4 Results and Discussion
		11.4.1 Discussion
	11.5 Conclusion
	References
Chapter 12 Integrating Metaheuristics and Machine Learning for Wellbeing Management: Case of COVID-19
	12.1 Introduction
	12.2 Related Work
		12.2.1 Modeling Non-Pharmaceutical COVID-19 Responses Cross Sectors
		12.2.2 Modeling COVID-19 Responses for Schools’ Management
		12.2.3 Modeling the Impact of Vaccines in Curbing the Outbreak
	12.3 Background Knowledge
		12.3.1 Machine Learning Techniques
		12.3.2 Deep Learning
		12.3.3 Genetic Algorithms
	12.4 Methodology
		12.4.1 Data Preparation
		12.4.2 Feature Engineering
		12.4.3 Model Selection
	12.5 Results and Discussions
		12.5.1 Model Validation
	12.6 Conclusion
	References
Chapter 13 Fusing Sentiment Analysis with Hybrid Collaborative Algorithms for Enhanced Recommender Systems
	13.1 Introduction
		13.1.1 Analysis of Sentiment
		13.1.2 Collaboration Filtering
			13.1.2.1 HCF-Based Recommender System
	13.2 Literature Survey
	13.3 Comparative Result Study
	13.4 Conclusion and Future Scope
	References
Chapter 14 The Future of Well-Being: AI-Powered Health Management with Privacy at its Core
	14.1 Introduction
		14.1.1 Challenges in Traditional Wellness Management
		14.1.2 AI Accelerators: A Game-Changer
		14.1.3 The Privacy Revolution of Federated Learning
		14.1.4 Objectives
		14.1.5 Contributions
	14.2 Related Works
	14.3 Proposed Work
		14.3.1 Secure Data Access with Federated Identity
		14.3.2 Blockchain-Powered Data Sharing: Revolutionizing Patient Data Management
		14.3.3 AI-Powered Analytics for Personalized Care
		14.3.4 Privacy-Preserving AI Through Federated Learning
	14.4 Performance Evaluation
		14.4.1 Model Accuracy
		14.4.2 Privacy Preservation
		14.4.3 Metrics Comparison Across Systems
	14.5 Conclusion and Future Work
	References
Chapter 15 Artificial Pancreas: Enhancing Glucose Control and Overall Well-Being
	15.1 Introduction
		15.1.1 Glucose Monitoring
		15.1.2 Insulin Pumps
	15.2 Closed-Loop Diabetes Control System
	15.3 Testing and Regulatory Approvals
	15.4 Safety Requirements in the Design of Artificial Pancreas
		15.4.1 General Safety Requirements
		15.4.2 Sensor Disturbance
		15.4.3 Insulin Pumps
		15.4.4 Control Algorithm
		15.4.5 Software/Network Vulnerabilities
		15.4.6 Profusion Site
		15.4.7 Meal and Other Disturbances
		15.4.8 Insulin Sensitivity
	Conclusion
	References
Index