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Computational Intelligence in Biomedical Internet of Medical Things

Edited by Satya Prakash Yadav, Abhay Bansal and Victor Hugo C. de Albuquerque
Copyright: 2026   |   Expected Pub Date:2026/02/28
ISBN: 9781394386628  |  Hardcover  |  
478 pages

One Line Description
Through comprehensive insights and real-world case studies, this book features in-depth knowledge of key concepts relating to optimizing biomedical IoMT systems.

Audience
Academics, research scholars, and industry professionals in the fields of mathematics, computer science, information technology, and health science.

Description
Biomedical Internet of Medical Things is a technological paradigm encompassing a range of technologies that enable machines to mimic human intelligence. Machine and deep learning algorithms facilitate self-learning for the discovery of hidden patterns, associations, and risks from voluminous datasets. Computer vision and natural language processing are prominent applications of AI, allowing machines to see and understand the world in ways previously only possible for humans. In healthcare, generative techniques can analyze large and complex datasets from wearable sensors, identifying patterns and trends that can aid in detecting, diagnosing, and monitoring chronic diseases. This book comprehensively consolidates the latest technologies, groundbreaking research, and practical applications of computational intelligence in biomedical IoMT, with a strong emphasis on optimizing healthcare information systems.
Readers will find the volume:
• Explores the transformative role of computational intelligence in the Internet of Medical Things (IoMT), demonstrating how intelligent systems enhance healthcare efficiency, accuracy, and patient-centric solutions at various scales;
• Examines key computational intelligence techniques and algorithms used in modern biomedical IoMT applications, emphasizing their impact on real-time diagnostics, personalized treatment, and remote patient monitoring;
• Highlights the evolution of AI-driven paradigms in biomedical IoMT, showcasing their role in predictive analytics, automated decision-making, and adaptive healthcare systems;
• Investigates the integration of trust management and advanced cybersecurity frameworks in intelligent healthcare networks.

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Author / Editor Details
Satya Prakash Yadav, PhD is an Associate Professor in the Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India with more than 17 years of experience. He has published four books, two patents, and many research articles in international journals and conferences. His research focuses on image processing, information retrieval, and feature extraction and programming.

Abhay Bansal, PhD is a Professor and the Dean of the School of Computer Science Engineering and Technology and the Dean of International Affairs and Corporate Outreach, Bennett University, Greater Noida , UP, India with more than 29 years of research and teaching experience. He has published more than 130 papers in various international journals and conferences of repute. His research interests include data science, data analytics, cloud computing, and data structure.

Victor Hugo C. de Albuquerque, PhD is a Professor and Senior Researcher in the Department of Teleinformatics Engineering, Federal University of Ceará, Brazil. He is a full member of the Brazilian Society of Biomedical Engineering and serves as an editor for several international journals and conferences. He has experience in biomedical science and engineering, mainly in the fields of applied computing, intelligent systems, and visualization and interaction.

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Table of Contents
Preface
Part I: Foundations and Frameworks
1. Introduction to Computational Biomedical Intelligence
G. Padma Priya, Anima Nanda, Nimisha Ghosh, E. Sakthivel, Devendra Parmar and Rakesh Kumar Yadav
1.1 Introduction
1.2 Emergence of Computational Biomedical Intelligence
1.3 Applications Driving Innovation in Healthcare
1.4 Bridging Biomedical Complexity with Advanced Computing
1.5 Prospects and Challenges
1.6 Conclusion
References
2. Fundamentals of Biomedical Data and Social Determinants
Girija Shankar Sahoo, D. Alex Anand, Manoranjan Parhi, Abdul Khadar A., Subbulakshmi Ganesan and Swapnil M. Parikh
2.1 Introduction
2.2 Methodology
2.2.1 Dataset
2.2.2 Preprocessing
2.3 Models of Machine Learning and Deep Learning are Employed to Predict Cardiovascular Diseases
2.4 Results
2.5 Conclusion
References
3. The Impact of Biomedical Intelligence on Public Health
Smitha Madhavan, Malathi H., Anjali R. Kumbar, Bethanney Janney J., Debahuti Mishra and Ritesh Kumar
3.1 Introduction
3.2 Related Works
3.3 Proposed Methods
3.3.1 Data Collection
3.4 Challenges the Biomedical Intelligence in Public Healthcare
3.5 Impact of Public Health on Disease Prediction Using Machine Learning
3.6 Impact of Public Health in Disease Prediction Using Deep Learning
3.7 Result and Discussion
3.8 Discussion
3.9 Conclusion
References
4. Healthcare Data Integration and Management
Swaroop Mohanty, Piyali Roy Chowdhury, Renuka Jyothi S., Satya Ranjan Das and Dhananjay Kumar Yadav
4.1 Introduction
4.2 Methodology
4.2.1 Selection Criteria
4.2.1.1 Inclusion Criteria
4.2.1.2 Exclusion Criteria
4.2.2 Challenges and Benefits of Healthcare Data Integration
4.3 Results
4.3.1 Discussion
4.4 Conclusion
References
5. Ethics and Legal Issues in Computational Biomedical Intelligence
Mohitkumar Jagdishchandra Rathod, Neha Arora, Sindu Divakaran, Ankita Agarwal, Suhas Ballal and Chinmaya Kumar Mohapatra
5.1 Introduction
5.2 Description of Biomedical Technological Growth and Development
5.2.1 Advancement and Obstacles of Biomedical Innovation
5.2.2 Identification and Aspects of Modern Biomedical Technologies
5.2.3 The Application of Ethical Principles in Biomedical Science and Technology
5.3 Ethical Considerations in Computational Biomedical Intelligence
5.3.1 Ethical Perspective
5.3.2 Ethical and Regulatory Considerations
5.3.3 Principles of Biomedical Ethics in the Use of Artificial Intelligence in Medical Education
5.3.4 Self-Determination
5.3.5 Equality
5.3.6 Beneficence
5.3.7 Lack of Malice
5.4 Legal Considerations in Computational Biomedical Intelligence
5.4.1 Legal Control and Biomedical Moral Policy
5.4.2 Strategies for Legal Effects on the Advancement of Biomedical Technologies
5.5 Result
5.6 Discussion
5.7 Conclusion
References
Part II: Advanced Analytical Techniques
6. Bioinstrumentation and Medical Devices
Roopashree R., Utpalkumar B. Patel, Krishnakumar Samikan, Joe Arun Raja, Preeti Naval and Aneesh Wunnava
6.1 Introduction
6.2 Computational Intelligence Techniques in the Internet of Medical Things
6.2.1 Using Computational Intelligence Techniques in Bioinstrumentation
6.2.1.1 The Role of Artificial Intelligence in Establishing a Smart Sensor Network
6.2.2 Applications of CI Across Devices
6.2.2.1 Remote Patient Monitoring
6.2.2.2 IoMT-Enabled Devices
6.2.3 Effective Use of CI to Enhance Bioinstrumentation and Medical Equipment
6.2.3.1 Deep Learning for Diagnostics
6.2.3.2 Remote Patient Monitoring with IoMT
6.2.4 Utilizing CI in Medical Devices and Bioinstrumentation
6.2.4.1 Diagnostic Applications (Imaging Systems)
6.2.4.2 Applications in Monitoring
6.2.5 Limitations and Difficulties
6.2.5.1 Ethical and Regulatory Concerns
6.2.5.2 Data Quality and Security Issues in IoMT Systems
6.2.5.3 Integration of CI with Existing Medical Infrastructure
6.2.6 Prospects and Patterns for the Future
6.2.6.1 Heavy Dependence on Edge Computing
6.2.6.2 5G-Connected Real-Time Monitoring Device
6.2.6.3 Potential for Next-Generation Bioinstrumentation Devices Enhanced by CI
6.2.7 Artificial Intelligent (AI)-Based Diagnostic Applications
6.3 Conclusion
References
7. Biomedical Signal Processing for Community Health
Sheuli Sen, Santanu Kumar Sahoo, Rekha M.M., Jemmy Christy H., Sumitra Menaria and Pooja Shukla
7.1 Introduction
7.1.1 Contributions
7.2 Literature Reviews
7.3 Methodology
7.3.1 Data Collection
7.3.2 Preprocessing Using Min-Max Scaler
7.3.3 Feature Extraction Using Wavelet Transform
7.3.4 Biomedical Signal Processing for Community Health Using White Shark Optimized Refined Random Forest
7.3.4.1 Refined Random Forest
7.3.4.2 White Shark Optimization
7.4 Results
7.4.1 Accuracy
7.4.2 Root Mean Squared Error
7.4.3 Mean Absolute Error
7.5 Discussion
7.6 Conclusion
References
8. Medical Image Processing and Analysis
Pallavi. M., Sarbeswar Hota, Ankita Gandhi, Anbarasi Jebaselvi G. D., G. Padma Priya and Divyanshi Rajvanshi
8.1 Introduction
8.2 Related Work
8.3 Methodology
8.3.1 Data Collection
8.3.2 Data Preprocessing Using Denoising
8.3.3 Image Segmentation Using k-Means Clustering
8.3.4 Feature Extraction Using a Convolutional Neural Network
8.3.5 Medical Image Processing and Analysis Using Enriched Whale-Optimized Logistic Regression
8.3.5.1 Logistic Regression
8.3.5.2 Enriched Whale Optimized
8.4 Result
8.4.1 F1-Score
8.5 Discussion
8.6 Conclusion
References
9. Genomic and Proteomic Data Analysis for Population Health
Roja Lakshmi Karri, Anand Prakash, Suman Sau, Subbulakshmi Ganesan, Jayshree Nellore and Shweta Singh
9.1 Introduction
9.2 Methodology
9.2.1 Genomes
9.2.2 Proteomic
9.2.3 Sequence of Important DNA
9.3 Human Genome Project
9.3.1 Human Genetic Variation
9.3.2 The Significance of Protein Structure
9.3.3 Database
9.3.4 Types of Databases
9.3.5 Difference between Nucleic Acid and Protein Database
9.3.6 Developments in Throughput Technology
9.4 Information Synthesis and Integration
9.4.1 Unstructured and Non-Standard Data
9.4.2 Processing Complex Queries
9.4.3 Integration of Data from Various Related Databases
9.4.4 Needed Features vs Current Capabilities
9.4.5 Key Areas of Focus for Data Management Study
9.4.6 Data Management Solutions
9.4.7 Proteomic and Genomic Data Analysis
9.5 Conclusion
References
10. Telemedicine and Remote Monitoring
Sheuli Sen, Girija Shankar Sahoo, Anjali R. Kumbhar, Sibun Parida, Aranganathan A. and Malathi H.
10.1 Introduction
10.2 Related Works
10.3 Methodology
10.3.1 Dataset Collection
10.3.2 Remote Monitoring Using ML
10.4 Experimental Results
10.4.1 Performance Metrics
10.5 Discussion
10.6 Conclusion
References
11. Machine Learning and Artificial Intelligence in Biomedical Applications
Deepti Pandey, Indraah Kolandaisamy, Visvesvaran C., Renuka Jyothi S., Suraya Mubeen and Prabhat Kumar Sahu
11.1 Introduction
11.2 Levels of Artificial Intelligence Applications Across Biomedical Subfields
11.3 Artificial Intelligence-Based Medical Imaging
11.3.1 Cardiovascular Health
11.3.2 Neurological Condition
11.3.3 Cancer Detection
11.3.4 Brain Tumor Identification
11.3.5 Muscle Injury Identification
11.3.6 Radiation Dosage
11.4 Different Diagnosis Using Machine Learning and Artificial Intelligence
11.5 Performance Evaluation of Machine Learning and Deep Learning Methods in Biomedical Application
11.6 Transformative Impact of Artificial Intelligence in Healthcare
11.7 Conclusion
References
12. The Internet of Medical Things in Chronic Disease Management
Ravindra Pandey, Shaktijeet Mohapatra, Suhas Ballal, Ritesh Kumar, Raesa Razeen and Bavanilatha M.
12.1 Introduction
12.1.1 Contributions of this Research
12.2 Related Works
12.3 Methodology
12.3.1 Data Collection
12.3.2 Preprocessing Using Min-Max Normalization
12.3.3 Feature Extraction Using Principal Component Analysis
12.3.4 Fine-Tuned Butterfly-Optimized Random Forest
12.3.5 Random Forest
12.3.6 Fine-Tuned Butterfly Optimization
12.4 Results
12.5 Discussion
12.6 Conclusion
12.6.1 Limitations and Future Research
References
13. Wearable Technologies in Internet of Medical Things Biomedical Intelligence
Ramesh B. Darla, Biswaranjan Swain, Roopashree R., Amreen Khanum D., Trapty Agarwal and Barani Selvaraj
13.1 Introduction
13.2 Related Articles
13.3 Methodology
13.3.1 Data Collection
13.3.2 Preprocessing
13.3.3 Feature Extraction
13.3.4 Intelligent Dwarf Mongoose Fine-Tuned Resilient LightGBM
13.3.5 Resilient LightGBM
13.3.6 Intelligent Dwarf Mongoose
13.4 Result and Discussion
13.4.1 Discussion
13.5 Conclusion
13.5.1 Limitation and Future Scope
References
Part III: Systems and Applications
14. Health Informatics and Community Decision Support Systems
Sheuli Sen, S. Jayashree, Sudhanshu Shekhar Bisoyi, Rekha M.M., Raesa Razeen and Ankita Agarwal
14.1 Introduction
14.2 Methodology
14.3 Patient-Centered Care through Healthcare Informatics
14.4 Clinical Decision Support Systems Framework
14.4.1 Knowledge Base
14.5 Telemedicine Framework
14.6 Telehealth Framework
14.7 Conclusion
References
15. Healthcare Data Acquisition and Management
G. Padma Priya, Indumathi S.M., Rahul Priyadarshi, Brijesh Vala, Preeti Naval and Kumari K.
15.1 Introduction
15.2 Healthcare Data Framework
15.2.1 Data Acquisition
15.2.2 Data Preprocessing
15.2.3 Data Storage
15.2.4 Data Management
15.2.5 Data Analytics
15.2.6 Data Visualization
15.3 Assessment of Healthcare Data
15.3.1 Accuracy
15.4 Conclusion
References
16. Telemedicine and Remote Health Monitoring for Underserved Communities
Sheuli Sen, Subbulakshmi Ganesan, Rohini Chavan, Deepak Dasaratha Rao, Binayak Pand and Ayesha Taranum
16.1 Introduction
16.2 Accessing Community Needs and Building Awareness
16.2.1 Community Input Required
16.2.2 Building Awareness
16.3 Establishing Infrastructure and Ensuring Accessibility
16.3.1 Electronic Medical Records System
16.3.2 Electronic Health Record
16.3.3 Personal Health of Records
16.3.4 Data Interchange Capabilities
16.4 Implementing Remote Health Monitoring and Device Distribution
16.5 Facilitating Telemedicine and Providing Ongoing Support
16.5.1 Telemedicine is Necessary for the Healthcare System
16.5.2 Telemedicine’s Features and Capabilities When Integrated into a Healthcare Management System
16.5.3 Providing Ongoing Support
16.6 Evaluating Effectiveness and Continuing Support
16.7 Result
16.8 Conclusion
References
17. Personalized Medicine and Computational Approaches
Dheeraj Kumar Singh, Sampath A. K., Balasankar Karavadi, Malathi H., Sarita Mahapatra and Pooja Shukla
17.1 Introduction
17.2 Biomedical Internet of Medical Things
17.3 Computational Approaches in Personalized Medicine
17.3.1 Machine Learning in Personalized Medicine
17.3.2 Deep Learning in Personalized Medicine
17.4 Bioinformatics and Multi-Omics in Personalized Medicine
17.5 Diagnosis and Treatment in Personalized Medicine
17.5.1 Personalized Diagnosis
17.5.2 Personalized Treatment
17.5.3 Clinical Decision Support Systems
17.6 Major Disease in Personalized Medicine
17.6.1 Cancer
17.6.2 Cardiovascular Diseases
17.6.3 Neurological Disorders
17.7 Computational Approaches and Accuracy Outcomes in Personalized Medicine Studies
17.8 Discussions
17.9 Summary and Future Directions
References
18. Internet of Medical Things and Social Health
Divyanshi Rajvanshi, Sumitra Menaria, Rajan Thangamani, Roselin Jenifer D., Renuka Jyothi S. and Alakananda Tripathy
18.1 Introduction
18.2 The Internet-of-Medical-Things Architecture
18.3 Methodology
18.3.1 Data Collection
18.3.2 Pre-Processing
18.3.3 Feature Extraction
18.4 Healthcare Monitoring Using Artificial Intelligence
18.5 Result
18.6 Discussion
18.7 Conclusion
References
Part IV: Innovations and Future Directions
19. Bioinformatics Algorithms and Tools
Suhas Ballal, Pooja Shukla, Hetal Bhaidasna, Riyazulla Rahman J., Y. Swarna Latha and Abhilash Pati
19.1 Introduction
19.2 Methodology
19.3 Bioinformatics’ Goals
19.4 Biological Sequence Examination
19.4.1 Pairwise Sequence Alignment
19.4.2 Multiple Sequence Alignment
19.5 Basic Local Alignment Search Tool
19.6 Comprehensive Genome-to-Proteome Examines
19.7 Gene Expression and Functional Examination
19.8 Challenges and Future Directions
19.9 Conclusion
References
20. Biomedical Data Acquisition and Management
Praveen Priyaranjan Nayak, Roopashree R., Deeksha Choudhary, Jay Gandhi, Asif Mohamed H. B. and Poonguzhali S.
20.1 Introduction
20.2 Literature Review
20.3 Internet of Medical Things Technologies and Their Role in Biomedical Data Acquisition
20.4 The Internet of Medical Things Data Acquisition
20.4.1 Raw and Original Data
20.4.2 Role of Metadata
20.4.3 Sensitive Data in Biomedical Study
20.4.4 Biomedical Measurement Systems Based on the Internet of Medical Things
20.4.5 Data Pre-Processing
20.4.6 Data Cleaning and Its Impact on Quality
20.4.7 Risk of Aggressive Data Cleaning
20.4.8 Normalization and Transformation for Enhanced Analysis
20.4.9 Challenges in High and Low Throughput Datasets in Biomedical
20.4.10 High Throughput
20.4.11 Low Throughput
20.5 Biomedical Data Management
20.5.1 Characteristics of the Data Management System
20.5.2 Data Storage and Security
20.5.3 Storage Procedures
20.5.4 Data Security Measures
20.5.5 Data Analysis and Interpretation
20.5.6 Data Sharing and Interoperability in Biomedical Data Management
20.6 Assessment of Security
20.7 Conclusion
References
21. Biomedical Data Integration and Fusion
Smita Rath, Emalda Roslin S., Rekha M.M., Awakash Mishra, Gaurav Kumar Ameta and Megha D. Bengalur
21.1 Introduction
21.1.1 Contributions
21.2 Literature Reviews
21.3 Methodology
21.3.1 Data Collection
21.3.2 OASIS Alzheimer’s Detection
21.3.3 Alzheimer Multiclass Image Data
21.3.4 Data Preprocessing Using the Weiner Filter
21.3.5 Feature Extraction Using Grey Level Co-Occurrence Matrix
21.3.6 Data Integration and Fusion Using Feature-Level Fusion
21.3.7 Biomedical Data Integration and Fusion Using Tabu Search Optimized Dynamic Random Forest
21.3.8 Dynamic Random Forest
21.3.9 Tabu Search Optimization
21.4 Results
21.4.1 Comparison Between Binary Class Classification and Multi-Class Classification Using Stages of Alzheimer’s Disease Data
21.4.2 Accuracy Comparison Between With and Without Optimization
21.5 Discussion
21.6 Conclusion
References
22. Biomedical Robotics and Assistive Technologies
Prasad P. S., Rourab Paul, Joany R. M., G. Padma Priya, Sweta Jethava and Ankita Thakur
22.1 Introduction
22.2 Surgical Robotics
22.2.1 Human-Robot Interaction
22.2.2 Haptics
22.2.3 Leading Surgical Robotic System
22.3 Rehabilitation Robotics
22.3.1 Advancing Rehabilitation Through Robotics
22.4 Assistive Robotics
22.4.1 Advancements and Challenges in Human-Robot Collaboration for Assistive Robotics
22.5 Wearable Assistive Robotics
22.5.1 Hybrid Wearable Assistive Robotics: Advancing Adaptability and Intelligence
22.6 Result
22.7 Discussion
22.8 Conclusion
References
23. Predictive Modeling in Healthcare
Trapty Agarwal, Lambodar Jena, Tabrej Mulla, Megalan Leo L. and Subbulakshmi Ganesan
23.1 Introduction
23.2 Methodology
23.2.1 Dataset
23.2.2 Data Preprocessing Using z-Score Normalization
23.2.3 Feature Extraction Using Principal Component Analysis
23.2.4 Refined Golden Jackal Fine-Tuned Random Forest
23.2.5 Random Forest
23.2.6 Refined Golden Jackal
23.2.7 Elite Opposition-Based Method of Instruction
23.2.8 The Simplex Method
23.3 Result
23.3.1 Accuracy
23.3.2 Precision
23.3.3 Recall
23.3.4 F1-Score
23.4 Discussion
23.5 Conclusion
References
24. Blockchain for Secure and Equitable Health Data Management
Ravindra Pandey, Arun Khatri, Amrutanshu Panigrahi, Malathi H., Intekhab Alam and Akshatha Y.
24.1 Introduction
24.2 Methodology
24.3 Blockchain
24.3.1 Blockchain Types and Key Features
24.3.2 Blockchain Layers and Protocols
24.3.3 Blockchain in Healthcare
24.3.4 Securing Patient Records
24.3.5 Medical Study Data Sharing
24.3.6 Management Data
24.3.7 Enhancing Privacy with Blockchain
24.3.8 Permissioned Blockchain for Healthcare
24.3.9 Patient-Controlled Data Access
24.3.10 Blockchain Potential in Healthcare
24.3.11 Challenges and Limitations of Blockchain in Healthcare
24.3.12 Future Prospects
24.4 Assessment of Healthcare Management
24.4.1 Parameters of Electronic Health Records
24.4.2 Computation Complexity
24.5 Conclusion
References
25. Quantum Computing in Biomedical Intelligence
Indraah Kolandaisamy, Renuka Jyothi S., Muthiah M.A., Bharat Jyoti Ranjan Sahoo, Sharath Kumar A.J. and Deeksha Choudhary
25.1 Introduction
25.2 Quantum Computing
25.3 Difference Between Classical and Quantum Computing
25.4 Quantum Computing in Machine Learning
25.5 Quantum Computing in Healthcare
25.6 Methodology
25.6.1 Dataset
25.7 Diagnosing Disease Using Quantum Computing
25.8 Impact of Quantum Computing on Drug Discovery
25.9 Result
25.10 Discussion
25.11 Conclusion
References
Index

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