Design and Forecasting Models for Disease Management

Pijush Dutta, PhD, is an assistant professor and head of the Department of Electronics and Communication Engineering at Greater Kolkata College of Engineering and Management, West Bengal, India, with over 11 years of teaching and over seven years of research experience. He has published eight books, as well as 14 patents and over 100 research articles in national and international journals and conferences. His research interests include sensors and transducers, nonlinear process control systems, the Internet of Things (IoT), and machine and deep learning. Sudip Mandal, PhD, is an assistant professor in the Electronics and Communication Engineering Department at Jalpaiguri Government Engineering College, India. He has over 50 publications in national and international peer-reviewed journals and conferences, as well as two Indian patents and two books. He is a member of the Institute of Electrical and Electronics Engineers’ Computational Intelligence Society. Korhan Cengiz, PhD, is an associate professor in the Department of Computer Engineering at Istinye University, Istanbul, Turkey. He has published over 40 articles in international peer-reviewed journals, five international patents, and edited over ten books. His research interests include wireless sensor networks, wireless communications, and statistical signal processing. Arindam Sadhu, PhD, is an assistant professor in the Electronics and Communication Engineering Department at Swami Vivekananda University, West Bengal, India, with over five years of teaching and over three years of research experience. He has published two international patents and over ten articles in national and international journals and conferences. His research interests include post-complementary metal-oxide-semiconductor transistors, quantum computing, and quantum dot cellular automata. Gour Gopal Jana is an assistant professor in the Electronics and Communication Engineering Department at Greater Kolkata College of Engineering and Management, West Bengal, India, with over 13 years of teaching and over three years of research experience. He has published two international patents and over ten research articles in national and international journals and conference proceedings. His research interests include metal thin film sensors, biosensors, nanobiosensors, and nanocomposites.

• Preface xviiPart 1: Safety and Regulatory Aspects for Disease Pre-Screening 11 A Study of Possible AI Aversion in Healthcare Consumers 3Tanupriya Mukherjee and Anusriya Mukherjee1.1 Introduction to AI in Healthcare 41.1.1 The Role of AI in Transforming Healthcare 51.1.2 The Unfolding Paradigm: Potential Benefits and Challenges of AI Implementation in Healthcare 61.1.3 Overview of Consumer Receptivity Towards AI in Medicine: A Comparative Analysis 71.2 Consumer Reluctance to Utilize AI in Healthcare: Present Scenario 81.2.1 Top Factors Influencing Consumer Resistance to Medical AI 101.2.2 Uncovering the Psychological Barriers and Concerns Associated with AI Adoption in Healthcare 111.2.3 Case Studies and Research Findings on Consumer Aversion to AI-Based Healthcare Services 131.2.4 Impact on Consumer Decision-Making 141.2.5 Effects of AI Aversion on Consumer Decision-Making Processes: An Analysis 151.2.6 Understanding How Consumer Perceptions Influence Their Choice Between Human and AI Healthcare Providers 151.2.7 Exploring Role of Trust, Perceived Competence and Empathy in Consumer Preferences 161.3 Economic Implications of AI Aversion 171.3.1 Investigating Influence of AI Aversion on Consumer Willingness to Pay for Healthcare Services 191.3.2 Influence of Patient Education on AI Aversion in Healthcare 191.3.3 Influence of Patient Awareness on AI Aversion in Healthcare 211.3.4 Influence of Age of Patient on AI Aversion in Healthcare 211.4 Overcoming Resistance to Medical AI 221.4.1 Strategies for Enhancing Consumer Trust and Acceptance of AI in Healthcare 231.4.2 Approaches to Alleviate Consumer Concerns and Misconceptions: Communication and Education 241.4.3 Cases of Successful Implementation of AI Technologies in Healthcare and Lessons Learned 251.5 Ethical Considerations and Governance 261.5.1 Regulatory Frameworks for Ethical AI Operations to Fight Aversion in Healthcare Consumers 271.5.2 Addressing the Potential Cost-Effectiveness and Affordability Concerns Associated with AI-Based Healthcare Solutions 281.5.3 Balancing Privacy, Data Protection and Need for Transparency in AI Healthcare Applications 291.6 Future Outlook and Opportunities 311.6.1 The Future of AI in Healthcare and Its Impact on Consumer Aversion 321.6.2 Exploring Emerging Technologies and Trends That May Alleviate Consumer Concerns 331.6.3 Opportunities for Collaboration Between AI Developers, Healthcare Providers, and Consumers 341.6.4 Summary of Key Findings on Consumer Aversion to AI in Healthcare 351.6.5 Implications for Healthcare Practitioners, Policymakers and Researchers 361.7 Conclusion 37References 382 A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector 47Anusriya Mukherjee, Tanupriya Mukherjee and Mili Mitra Roy2.1 Introduction 482.1.1 Understanding the Role of AI in Healthcare 492.1.2 Advantages of AI in Healthcare 502.1.3 Moral Dilemmas and AI-Based Healthcare 522.2 What is Integrated and Systematic Moral Cognitive Therapy (ISMCT)? 542.2.1 Integrating Moral Cognitive Therapy with AI 552.2.2 Alignment of Moral Cognitive Therapy Principles with AI Applications 562.2.3 Benefits of Integrated and Systematic Moral Cognitive Therapy 572.2.4 Applications of AI-Integrated Moral Cognitive Therapy in Healthcare 582.3 The Role of AI in Healthcare: A Fine Balance Between Ethics and Innovation 612.3.1 Humanizing Healthcare: Towards an AI-ISMCT 622.3.2 Synergized AI and ISMCT 632.3.3 Case Study and Success Stories 642.4 Advancing Research in AI-Integrated Moral Cognitive Therapy 672.4.1 Collaborative Efforts Between Healthcare Professionals and AI Developers 682.4.2 Implications for Policy and Regulatory Frameworks 692.5 Conclusion 70References 703 A Strategic Model to Control Non-Communicable Diseases 77Soumik Gangopadhyay, Amitava Ukil, Soma Sur and Saugat Ghosh3.1 Introduction 783.1.1 India and NCDs 783.2 Survey of Literature 843.2.1 Factors Contributing to the Growth of NCDs 843.2.2 Lifestyle Modification – A Strategic Role in Mitigation of NCD 853.2.3 Policy to Control NCDs 863.3 Proposed Model 873.3.1 Registration and Information Centre (RIC) 883.3.2 Integration Centre (IIC) 883.3.3 Strategic Review Centre (SRC) 893.3.4 Expected Outcome of the Proposed Model 903.4 Conclusion 91References 924 Image Compression Technique Using Color Filter Array (CFA) for Disease Diagnosis and Treatment 99Indrani Dalui, Avisek Chatterjee, Surajit Goon and Pubali Das Sarkar4.1 Introduction 1004.1.1 Color Filter Array 1004.1.2 Electronic Health Record (EHR) 1014.2 Related Works 1024.3 Proposed Model 1084.4 Implementation 1104.5 Results 1114.6 Conclusion 112References 1135 Research in Image Processing for Medical Applications Using the Secure Smart Healthcare Technique 115Debraj Modak and Chowdhury Jaminur Rahaman5.1 Introduction 1165.1.1 Imaging Systems 1185.1.2 The Digital Image Processing System 1195.1.3 Image Enhancement 1205.2 Classification of Digital Images 1215.2.1 Utilizations of Digital Image Processing (DIP) 1215.2.1.1 Medicine 1215.2.1.2 Forensics 1225.2.2 Medical Image Analysis 1225.2.3 Max-Variance Automatic Cut-Off Method 1225.2.4 Medical Imaging Segmentation 1245.2.5 Image-Based on Edge Detection 1245.2.5.1 Robert’s Kernel Method 1255.2.5.2 Prewitt Kernel 1255.2.5.3 Sobel Kernel 1255.2.5.4 k-Means Segmentation 1265.2.6 Images from γ-Rays 1265.2.6.1 Non-Ionizing Radiation 1275.2.6.2 Magnetic Resonance Imaging 1285.2.6.3 Segmentation Using Multiple Images Acquired by Different Imaging Techniques 1295.3 Methods 1305.3.1 k-Means Approach 1305.3.2 Bayesian Objective Function 1325.4 Segmentation and Database Extraction with Neural Networks 1335.4.1 Artificial Neural Network 1335.4.2 Bayesian Belief Networks 1345.5 Applications in Medical Image Analysis 1355.5.1 Using Artificial Neural Network for Better Optimization and Detection in Medical Imaging 1365.5.1.1 Opportunities 1365.6 Standardize Analytics Pipeline for the Health Sector 1365.7 Feature Extraction/Selection 1385.7.1 Significance of Machine Learning for Medical Image Processing 1385.7.2 Significance of Deep Learning for Medical Image Processing 1395.8 Image-Based Forecasting Using Internet of Things (IoT) in Smart Healthcare System 1415.9 IoT Monitoring Applications Based on Image Processing 1435.10 Significance of Computer-aided Big Healthcare Data (BHD) for Medical Image Processing 1455.11 Applications of Big Data 1475.11.1 Big Data Analytics in Health Sector 1475.11.2 Computer-Aided Diagnosis in Mammography 1495.11.3 Tumor Imaging and Treatment 1495.11.4 Molecular Imaging 1495.11.5 Surgical Interventions 1505.12 Conclusion 150References 1516 Comparative Study on Image Enhancement Techniques for Biomedical Images 155Sudip Mandal, Uma Biswas, Aparna Mahato and Aurgha Karmakar6.1 Introduction 1566.2 Literature Review 1576.3 Theoretical Concepts 1586.3.1 Logarithmic Transformation 1596.3.1.1 Advantages of Log Transformation 1606.3.1.2 Limitations of Log Transformation 1606.3.2 Power Law Transformation or Gamma Correction 1606.3.2.1 Advantages of Gamma Correction 1616.3.2.2 Limitations of Gamma Correction 1616.3.3 Piecewise Linear Transformation or Contrast Stretching 1626.3.3.1 Advantages of Contrast Stretching 1626.3.3.2 Limitations of Contrast Stretching 1636.3.4 Histogram Equalization 1636.3.4.1 Advantages of Histogram Equalization 1646.3.4.2 Limitations of Histogram Equalization 1646.3.5 Contrast-Limited Adaptive Histogram Equalization (clahe) 1646.3.5.1 Advantages of CLAHE 1656.3.5.2 Limitation of CLAHE 1656.3.6 Adjustment Function 1666.4 Results and Discussion 1666.4.1 Images and Histograms for Different Images Using Different Enhancement Methods 1676.4.2 Comparison for Different Image Enhancement Techniques 1756.5 Conclusion 178References 1797 Exploring Parkinson’s Disease Progression and Patient Variability: Insights from Clinical and Molecular Data Analysis 181Amit Kumar, Neha Sharma and Korhan Cengiz7.1 Introduction 1827.2 Literature Review 1837.3 Data Review 1847.3.1 Clinical Data 1857.3.2 Peptides Data 1927.3.3 Protein Data 1947.4 Parkinson’s Dynamic for Patients in Train 1967.5 Conclusion 197References 1988 A Survey-Based Comparative Study on Machine Learning Techniques for Early Detection of Mental Illness 201Prachi Majumder, Sompadma Mukherjee, Shreyashi Saha, Tamasree Biswas, Mousumi Saha, Deepanwita Das and Suchismita Maiti8.1 Introduction 2018.2 Background 2028.3 Review of Previous Works 2038.3.1 Standard Questionnaire 2038.3.2 Social Media Content 2068.4 Comparative Result 2088.5 Discussion 2128.6 Conclusion 213References 213Part 2: Clinical Decision Support System for Early Disease Detection and Management 2159 Diagnostics and Classification of Alzheimer’s Diseases Using Improved Deep Learning Architectures 217Mainak Dey, Pijush Dutta and Gour Gopal Jana9.1 Introduction 2189.2 Related Works 2199.3 Method 2229.3.1 Data Description 2249.4 Result Analysis 2259.4.1 Performance Metrics 2279.4.2 Experimental Setup 2309.5 Conclusion 232Data Availability 233References 23310 Perform a Comparative Study Based on Conventional Machine Learning Approaches for Human Stress Level Detection 237Pratham Sharma, Prerana Singh, Mahe Parah, Shyamapriya Chatterjee, Anirban Bhar, Soumya Bhattacharyya and Pijush Dutta10.1 Introduction 23810.2 Related Work 23910.3 Architecture Design 24210.3.1 Body Temperature 24310.3.2 Humidity Analysis 24310.3.3 Step Count Analysis 24310.3.4 Dataset 24310.4 Experiment 24410.4.1 Performance Matrices 24510.5 Result Analysis 24610.6 Conclusion 248References 24911 Diabetes Prediction Using a Hybrid PCA-Based Feature Selection and Computational Machine Learning Algorithm 253Sumanta Dey, Pijush Dutta, Gour Gopal Jana and Arindam Sadhu11.1 Introduction 25411.2 Related Work 25411.3 Proposed Workflow 25611.3.1 Data Pre-Processing 25611.3.2 Feature Selection 25711.3.3 Dimensionality Reduction 25811.3.4 Classification 25911.4 Result Analysis 26111.4.1 Evaluation Criteria 26111.5 Conclusion and Future Work 265References 26612 A Robust IoT-Based Approach to Enhance Cybersecurity and Patient Trust in the Smart Health Care System: Zero-Trust Model 269Raghunath Maji, Biswajit Gayen and Sandeepan Saha12.1 Introduction 27012.2 Security Threats on Smart Healthcare 27112.2.1 Medical Data Monitoring and Patient Privacy Information 27112.2.2 Network Attacks on Critical Infrastructures 27212.2.3 Malicious Data Tampering 27212.3 Smart Healthcare Security and Four-Dimension Model 27312.3.1 Subject 27312.3.2 Object 27412.3.3 Environment 27512.3.4 Behavior 27512.3.5 Risk Assessment and Security Checking 27512.4 Conclusion and Future Prospects 279Acknowledgment 280References 28013 Safeguarding Digital Health: A Novel Approach to Malicious Device Detection in Smart Healthcare 283Raghunath Maji and Biswajit Gayen13.1 Introduction 28413.2 Related Work 28613.3 Our Proposed Framework 28913.4 Overview of Our Proposed Framework 28913.5 Evaluation Procedure 29113.6 Performance Evaluation 29213.7 Conclusion 293References 294Index 297