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Wellness Management Powered by AI Technologies

Edited by Bharat Bhushan, Akib Khanday, Khursheed Aurangzeb, Sudhir Kumar Sharma and Parma Nand
Series: Machine Learning in Biomedical Science and Healthcare Informatics
Copyright: 2025   |   Status: Published
ISBN: 9781394286997  |  Hardcover  |  
440 pages
Price: $225 USD
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One Line Description
This book is an essential resource on the impact of AI in medical systems, helping readers stay ahead in the modern era with cutting-edge solutions, knowledge, and real-world case studies.

Audience
This book is a valuable resource for researchers, industry professionals, and engineers from diverse fields such as computer science, artificial intelligence, electronics and electrical engineering, healthcare management, and policymakers.

Description
Wellness Management Powered by AI Technologies explores the intricate ways machine learning and the Internet of Things (IoT) have been woven into the fabric of healthcare solutions. From smart wearable devices tracking vital signs in real time to ML-driven diagnostic tools providing accurate predictions, readers will gain insights into how these technologies continually reshape healthcare.
The book begins by examining the fundamental principles of machine learning and IoT, providing readers with a solid understanding of the underlying concepts. Through clear and concise explanations, readers will grasp the complexities of the algorithms that power predictive analytics, disease detection, and personalized treatment recommendations. In parallel, they will uncover the role of IoT devices in collecting data that fuels these intelligent systems, bridging the gap between patients and practitioners.
In the following chapters, readers will delve into real-world case studies and success stories that illustrate the tangible benefits of this dynamic duo. This book is not merely a technical exposition; it serves as a roadmap for healthcare professionals and anyone invested in the future of healthcare.
Readers will find the book:
•Explores how AI is transforming diagnostics, treatments, and healthcare delivery, offering cutting-edge solutions for modern healthcare challenges;
•Provides practical knowledge on implementing AI in healthcare settings, enhancing efficiency and patient outcomes;
•Offers authoritative insights into current AI trends and future developments in healthcare;
•Features real-world case studies and examples showcasing successful AI integrations in various medical fields.

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Author / Editor Details
Bharat Bhushan, PhD, is an assistant professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India. He has published more than 150 research papers, contributed over 30 book chapters, and edited 20 books.

Akib Khanday, PhD, is a post-doctoral research fellow in the Department of Computer Science and Software Engineering-CIT, United Arab Emirates University, Abu Dhabi, United Arab Emirates. His research interests include computational social sciences, natural language processing (NLP), and machine/deep learning.

Khursheed Aurangzeb, PhD, is an associate professor in the Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. Over his 15 years of research, he has been involved in several projects related to machine/deep learning and embedded systems. His research interests focus on computer architecture, signal processing, and wireless sensor networks.

Sudhir Kumar Sharma, PhD, is a professor and head of the Department of Computer Science at the Institute of Information Technology & Management, affiliated with GGSIPU, New Delhi, India. His research interests include machine learning, data mining, and security. He has published more than 60 research papers in various international journals and conferences and is the author of seven books in the fields of IoT, wireless sensor networks (WSN), and blockchain.

Parma Nand, PhD, is the dean of the School of Engineering and Technology, Sharda University, Greater Noida, India. His expertise includes wireless and sensor networks, cryptography, algorithms, and computer graphics. He has published more than 85 papers in peer-reviewed journals and filed two patents.

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Table of Contents
Preface
1. Exploring Functional Modules Using Co-Clustering of Protein Interaction Networks
R. Gowri and R. Rathipriya
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
2. Natural Language Processing in Healthcare: Enhancing Wellbeing through a COVID-19 Case Study
Akib Mohi Ud Din Khanday, Salah Bouktif and Ali Ouni
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
3. Artificial Intelligence Assisted Internet of Medical Things (AIoMTs) in Sustainable Healthcare Ecosystem
Wasswa Shafik
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
4. An Online Platform for Timely Access to Medical Care with the Help of Real-Time Data Analysis
Pancham Singh and Mrignainy Kansal
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
5. A Comprehensive Review of Cardiac Image Analysis for Precise Heart Disease Diagnosis Using Deep Learning Techniques
Anuj Gupta, Vikas Kumar and Aryan Nakhale
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
6. A Hybrid Machine Learning Model for an Efficient Detection of Liver Inflammation
Hema Ramachandran and Syedakbar Syed Yusuff
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
7. Advancements in Parkinson’s Disease Diagnosis through Automated Speech Analysis
P. Deepa, Rashmita Khilar and Saumendra Kumar Mohapatra
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
8. Public Opinion Segmentation on COVID-19 Vaccination and Its Impact on Wellbeing
Akib Mohi Ud Din Khanday, Salah Bouktif and K. Nimmi
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
9. Revolutionizing Healthcare with IoT in Cardiology
Aafreen Jan, K. Nimmi and Mohd Anas Wajid
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
10. Human Biological Analysis Through Fitness Watch Using Deep Learning Algorithm
Nilesh Bhaskarrao Bahadure, Ramdas Khomane, Anjali Singh, Anisha Jaiswal, Rashmi Kadu, Rohini Bharne, Bhumika Kosarkar and Sidheswar Routray
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
11. Decoding Kidney Health: Effectiveness of Machine Learning Techniques in Diagnosis of Chronic Kidney Disease
Suhail Rashid Wani, Syed Naseer Ahmad Shah, Roshni Afshan and Asif Adil
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
12. Integrating Metaheuristics and Machine Learning for Wellbeing Management: Case of COVID-19
Safea Matar Al Senani and Salah Bouktif
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
13. Fusing Sentiment Analysis with Hybrid Collaborative Algorithms for Enhanced Recommender Systems
Anindya Nag, Md. Mehedi Hassan, Mohammad Abu Tareq Rony, Biva Das, Riya Sil,
Prianka Saha, Pronab Sarker and Anupam Kumar Bairagi
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
14. The Future of Well-Being: AI-Powered Health Management with Privacy at its Core
D. Dhinakaran, S. Edwin Raja, J. Jeno Jasmine, P. Vimal Kumar and R. Ramani
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
15. Artificial Pancreas: Enhancing Glucose Control and Overall Well-Being
Owais Bhat, Syed Tanzeel Rabani, Syed Mohsin Saif, Zubair Jeelani and Nawaz Ali Lone
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

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