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Design and Forecasting Models for Disease Management

Edited by Pijush Dutta, Sudip Mandal, Korhan Cengiz, Arindam Sadhu, and Gour Gopal Jana
Copyright: 2024   |   Expected Pub Date:2024/08/30
ISBN: 9781394234042  |  Hardcover  |  
322 pages

One Line Description
The book provides an essential overview of AI techniques in disease management
and how these computational methods can lead to further innovations in healthcare.

Audience
Researchers, engineers and graduate students in the fields of computational biology, information technology, bioinformatics, and epidemiology.

Description
Design and Forecasting Models for Disease Management is a resourceful volume of 13 chapters that elaborates on computational methods and how AI techniques can aid in smart disease management. It contains several statistical and AI techniques that can be used to acquire data on many different diseases. The main objective of this book is to demonstrate how AI techniques work for early disease detection and forecasting useful information for medical experts. As such, this volume intends to serve as a resource to elicit and elaborate on possible intelligent
mechanisms for helping detect early signs of diseases. Additionally, the book examines numerous machine learning and data analysis techniques in the biomedical field that are used for detecting and forecasting disease management at the cellular level. It discusses various applications of image segmentation, data analysis techniques, and hybrid machine learning techniques for illnesses, and encompasses modeling, prediction, and diagnosis of disease data.

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Author / Editor Details
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.

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.

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Table of Contents
Preface
Part 1: Safety and Regulatory Aspects for Disease Pre-Screening
1. A Study of Possible AI Aversion in Healthcare Consumers
Tanupriya Mukherjee and Anusriya Mukherjee
1.1 Introduction to AI in Healthcare
1.1.1 The Role of AI in Transforming Healthcare
1.1.2 The Unfolding Paradigm: Potential Benefits and Challenges of AI Implementation in Healthcare
1.1.3 Overview of Consumer Receptivity Towards AI in Medicine: A Comparative Analysis
1.2 Consumer Reluctance to Utilize AI in Healthcare: Present Scenario
1.2.1 Top Factors Influencing Consumer Resistance to Medical AI
1.2.2 Uncovering the Psychological Barriers and Concerns Associated with AI Adoption in Healthcare
1.2.3 Case Studies and Research Findings on Consumer Aversion to AI-Based Healthcare Services
1.2.4 Impact on Consumer Decision-Making
1.2.5 Effects of AI Aversion on Consumer Decision-Making Processes: An Analysis
1.2.6 Understanding How Consumer Perceptions Influence Their Choice Between Human and AI Healthcare Providers
1.2.7 Exploring Role of Trust, Perceived Competence and Empathy in Consumer Preferences
1.3 Economic Implications of AI Aversion
1.3.1 Investigating Influence of AI Aversion on Consumer Willingness to Pay for Healthcare Services
1.3.2 Influence of Patient Education on AI Aversion in Healthcare
1.3.3 Influence of Patient Awareness on AI Aversion in Healthcare
1.3.4 Influence of Age of Patient on AI Aversion in Healthcare
1.4 Overcoming Resistance to Medical AI
1.4.1 Strategies for Enhancing Consumer Trust and Acceptance of AI in Healthcare
1.4.2 Approaches to Alleviate Consumer Concerns and Misconceptions: Communication and Education
1.4.3 Cases of Successful Implementation of AI Technologies in Healthcare and Lessons Learned
1.5 Ethical Considerations and Governance
1.5.1 Regulatory Frameworks for Ethical AI Operations to Fight Aversion in Healthcare Consumers
1.5.2 Addressing the Potential Cost-Effectiveness and Affordability Concerns Associated with AI-Based Healthcare Solutions
1.5.3 Balancing Privacy, Data Protection and Need for Transparency in AI Healthcare Applications
1.6 Future Outlook and Opportunities
1.6.1 The Future of AI in Healthcare and Its Impact on Consumer Aversion
1.6.2 Exploring Emerging Technologies and Trends That May Alleviate Consumer Concerns
1.6.3 Opportunities for Collaboration Between AI Developers, Healthcare Providers, and Consumers
1.6.4 Summary of Key Findings on Consumer Aversion to AI in Healthcare
1.6.5 Implications for Healthcare Practitioners, Policymakers and Researchers
1.7 Conclusion
References
2. A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector
Anusriya Mukherjee, Tanupriya Mukherjee and Mili Mitra Roy
2.1 Introduction
2.1.1 Understanding the Role of AI in Healthcare
2.1.2 Advantages of AI in Healthcare
2.1.3 Moral Dilemmas and AI-Based Healthcare
2.2 What is Integrated and Systematic Moral Cognitive Therapy (ISMCT)?
2.2.1 Integrating Moral Cognitive Therapy with AI
2.2.2 Alignment of Moral Cognitive Therapy Principles with AI Applications
2.2.3 Benefits of Integrated and Systematic Moral Cognitive Therapy
2.2.4 Applications of AI-Integrated Moral Cognitive Therapy in Healthcare
2.3 The Role of AI in Healthcare: A Fine Balance Between Ethics and Innovation
2.3.1 Humanizing Healthcare: Towards an AI-ISMCT
2.3.2 Synergized AI and ISMCT
2.3.3 Case Study and Success Stories
2.4 Advancing Research in AI-Integrated Moral Cognitive Therapy
2.4.1 Collaborative Efforts Between Healthcare Professionals and AI Developers
2.4.2 Implications for Policy and Regulatory Frameworks
2.5 Conclusion
References
3. A Strategic Model to Control Non-Communicable Diseases
Soumik Gangopadhyay, Amitava Ukil, Soma Sur and Saugat Ghosh
3.1 Introduction
3.1.1 India and NCDs
3.2 Survey of Literature
3.2.1 Factors Contributing to the Growth of NCDs
3.2.2 Lifestyle Modification – A Strategic Role in Mitigation of NCD
3.2.3 Policy to Control NCDs
3.3 Proposed Model
3.3.1 Registration and Information Centre (RIC)
3.3.2 Integration Centre (IIC)
3.3.3 Strategic Review Centre (SRC)
3.3.4 Expected Outcome of the Proposed Model
3.4 Conclusion
References
4. Image Compression Technique Using Color Filter Array (CFA) for Disease Diagnosis and Treatment
Indrani Dalui, Avisek Chatterjee, Surajit Goon and Pubali Das Sarkar
4.1 Introduction
4.1.1 Color Filter Array
4.1.2 Electronic Health Record (EHR)
4.2 Related Works
4.3 Proposed Model
4.4 Implementation
4.5 Results
4.6 Conclusion
References
5. Research in Image Processing for Medical Applications Using the Secure Smart Healthcare Technique
Debraj Modak and Chowdhury Jaminur Rahaman
5.1 Introduction
5.1.1 Imaging Systems
5.1.2 The Digital Image Processing System
5.1.3 Image Enhancement
5.2 Classification of Digital Images
5.2.1 Utilizations of Digital Image Processing (DIP)
5.2.1.1 Medicine
5.2.1.2 Forensics
5.2.2 Medical Image Analysis
5.2.3 Max-Variance Automatic Cut-Off Method
5.2.4 Medical Imaging Segmentation
5.2.5 Image-Based on Edge Detection
5.2.5.1 Robert’s Kernel Method
5.2.5.2 Prewitt Kernel
5.2.5.3 Sobel Kernel
5.2.5.4 k-Means Segmentation
5.2.6 Images from γ-Rays
5.2.6.1 Non-Ionizing Radiation
5.2.6.2 Magnetic Resonance Imaging
5.2.6.3 Segmentation Using Multiple Images Acquired by Different Imaging Techniques
5.3 Methods
5.3.1 k-Means Approach
5.3.2 Bayesian Objective Function
5.4 Segmentation and Database Extraction with Neural Networks
5.4.1 Artificial Neural Network
5.4.2 Bayesian Belief Networks
5.5 Applications in Medical Image Analysis
5.5.1 Using Artificial Neural Network for Better Optimization and Detection in Medical Imaging
5.5.1.1 Opportunities
5.6 Standardize Analytics Pipeline for the Health Sector
5.7 Feature Extraction/Selection
5.7.1 Significance of Machine Learning for Medical Image Processing
5.7.2 Significance of Deep Learning for Medical Image Processing
5.8 Image-Based Forecasting Using Internet of Things (IoT) in Smart Healthcare System
5.9 IoT Monitoring Applications Based on Image Processing
5.10 Significance of Computer-aided Big Healthcare Data (BHD) for Medical Image Processing
5.11 Applications of Big Data
5.11.1 Big Data Analytics in Health Sector
5.11.2 Computer-Aided Diagnosis in Mammography
5.11.3 Tumor Imaging and Treatment
5.11.4 Molecular Imaging
5.11.5 Surgical Interventions
5.12 Conclusion
References
6. Comparative Study on Image Enhancement Techniques for Biomedical Images
Sudip Mandal, Uma Biswas, Aparna Mahato and Aurgha Karmakar
6.1 Introduction
6.2 Literature Review
6.3 Theoretical Concepts
6.3.1 Logarithmic Transformation
6.3.1.1 Advantages of Log Transformation
6.3.1.2 Limitations of Log Transformation
6.3.2 Power Law Transformation or Gamma Correction
6.3.2.1 Advantages of Gamma Correction
6.3.2.2 Limitations of Gamma Correction
6.3.3 Piecewise Linear Transformation or Contrast Stretching
6.3.3.1 Advantages of Contrast Stretching
6.3.3.2 Limitations of Contrast Stretching
6.3.4 Histogram Equalization
6.3.4.1 Advantages of Histogram Equalization
6.3.4.2 Limitations of Histogram Equalization
6.3.5 Contrast-Limited Adaptive Histogram Equalization (CLAHE)
6.3.5.1 Advantages of CLAHE
6.3.5.2 Limitation of CLAHE
6.3.6 Adjustment Function
6.4 Results and Discussion
6.4.1 Images and Histograms for Different Images Using Different Enhancement Methods
6.4.2 Comparison for Different Image Enhancement Techniques
6.5 Conclusion
References
7. Exploring Parkinson’s Disease Progression and Patient Variability: Insights from Clinical and Molecular Data Analysis
Amit Kumar, Neha Sharma and Korhan Cengiz
7.1 Introduction
7.2 Literature Review
7.3 Data Review
7.3.1 Clinical Data
7.3.2 Peptides Data
7.3.3 Protein Data
7.4 Parkinson’s Dynamic for Patients in Train
7.5 Conclusion
References
8. A Survey-Based Comparative Study on Machine Learning Techniques for Early Detection of Mental Illness
Prachi Majumder, Sompadma Mukherjee, Shreyashi Saha, Tamasree Biswas, Mousumi Saha, Deepanwita Das and Suchismita Maiti
8.1 Introduction
8.2 Background
8.3 Review of Previous Works
8.3.1 Standard Questionnaire
8.3.2 Social Media Content
8.4 Comparative Result
8.5 Discussion
8.6 Conclusion
References
Part 2: Clinical Decision Support System for Early Disease Detection and Management
9. Diagnostics and Classification of Alzheimer’s Diseases Using Improved Deep Learning Architectures
Mainak Dey, Pijush Dutta and Gour Gopal Jana
9.1 Introduction
9.2 Related Works
9.3 Method
9.3.1 Data Description
9.4 Result Analysis
9.4.1 Performance Metrics
9.4.2 Experimental Setup
9.5 Conclusion
Data Availability
References
10. Perform a Comparative Study Based on Conventional Machine Learning Approaches for Human Stress Level Detection
Pratham Sharma, Prerana Singh, Mahe Parah, Shyamapriya Chatterjee, Anirban Bhar, Soumya Bhattacharyya and Pijush Dutta
10.1 Introduction
10.2 Related Work
10.3 Architecture Design
10.3.1 Body Temperature
10.3.2 Humidity Analysis
10.3.3 Step Count Analysis
10.3.4 Dataset
10.4 Experiment
10.4.1 Performance Matrices
10.5 Result Analysis
10.6 Conclusion
References
11. Diabetes Prediction Using a Hybrid PCA-Based Feature Selection and Computational Machine Learning Algorithm
Sumanta Dey, Pijush Dutta, Gour Gopal Jana and Arindam Sadhu
11.1 Introduction
11.2 Related Work
11.3 Proposed Workflow
11.3.1 Data Pre-Processing
11.3.2 Feature Selection
11.3.3 Dimensionality Reduction
11.3.4 Classification
11.4 Result Analysis
11.4.1 Evaluation Criteria
11.5 Conclusion and Future Work
References
12. A Robust IoT-Based Approach to Enhance Cybersecurity and Patient Trust in the Smart Health Care System: Zero-Trust Model
Raghunath Maji, Biswajit Gayen and Sandeepan Saha
12.1 Introduction
12.2 Security Threats on Smart Healthcare
12.2.1 Medical Data Monitoring and Patient Privacy Information
12.2.2 Network Attacks on Critical Infrastructures
12.2.3 Malicious Data Tampering
12.3 Smart Healthcare Security and Four-Dimension Model
12.3.1 Subject
12.3.2 Object
12.3.3 Environment
12.3.4 Behavior
12.3.5 Risk Assessment and Security Checking
12.4 Conclusion and Future Prospects
Acknowledgment
References
13. Safeguarding Digital Health: A Novel Approach to Malicious Device Detection in Smart Healthcare
Raghunath Maji and Biswajit Gayen
13.1 Introduction
13.2 Related Work
13.3 Our Proposed Framework
13.4 Overview of Our Proposed Framework
13.5 Evaluation Procedure
13.6 Performance Evaluation
13.7 Conclusion
References
Index

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Description
Author/Editor Details
Table of Contents
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