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Psychopathology Early Prediction and Classification

A Deep Generative AI Approach
Edited by Abhishek Kumar, Pramod Singh Rathore, Sachin Ahuja, and Pankaj Rahi
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394336739 | Hardcover |
742 pages
Price: $225 USD

One Line Description
Unlock the full potential of artificial intelligence in mental health with this definitive guide to combining multiple machine learning models for reproducible biomarker discovery and predictive treatment modeling.

Audience
Mental health professionals, AI and machine learning researchers, healthcare data scientists, healthcare policymakers, bioinformatics professionals, technology developers and entrepreneurs, mental health advocates, and non-profit organizations experts who work at the intersection of information technology and biology.

Description
Advancing Psychopathology Diagnosis and Treatment: The Power of Ensemble Learning delves into the transformative potential of ensemble learning techniques in the field of psychopathology. This comprehensive book provides an in-depth exploration of how combining multiple machine learning models can enhance the accuracy of diagnoses, predict treatment outcomes, and refine therapeutic interventions for a variety of mental health disorders, including depression, anxiety, schizophrenia, and bipolar disorder. Key sections of the book examine the capabilities of ensemble learning in developing personalized treatment strategies that cater to individual patient needs and predicting treatment responses. It also explores the use of these advanced algorithms for biomarker discovery, enhancing the reproducibility of identifying biological indicators linked to mental health conditions. The book discusses the practical aspects of implementing these technologies in clinical settings, including integration with existing healthcare systems and clinician training. Through a blend of theoretical insights and practical examples, this book is an essential resource for clinicians, researchers, and policymakers involved in mental health care, offering innovative solutions and fostering a deeper understanding of how artificial intelligence can be harnessed to improve patient outcomes in psychopathology.

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Author / Editor Details
Abhishek Kumar, PhD is currently working an Associate Professor at Manipal University. He has more than 100 publications in reputed, peer-reviewed national and international journals, books, and conferences. His research interests include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning.

Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University with more than 11 years of academic experience. He has published more than 55 papers in reputable, peer-reviewed national and international journals, books, and conferences and co-authored and edited numerous books with well-known publishers. His research interests include NS2, computer networks, mining, and DBMS.

Sachin Ahuja, PhD is a Professor and Executive Director of UIE at Chandigarh University. He has led multiple funded research projects in artificial intelligence, machine learning, and data mining and has contributed to numerous academic books. He has also served as a guest editor for special issues in reputed international journals.

Pankaj Rahi, PhD is an Associate Professor in Health Information Technology Management at the Indian Institute of Health Management Research with more than 17 years of experience. He has published more than 15 research papers and book chapters, one book, and ten patents. His research experience is in smart eHealth systems, brain technology interface, cloud and big data analytics, machine learning, and artificial intelligence.

Table of Contents
Preface
1. Assessment of Well-Being Among Undergraduate Students Using PERMA+ Model
Mihir Vakhariya and Supriya S. Patil
1.1 Introduction
1.1.1 Unveiling Hidden Struggles: Exploring Well-Being in Medical Students
1.1.2 Beyond Conventions: Unveiling Well-Being through PERMA+ Model
1.1.3 Contextualizing Well-Being in Indian Medical Landscape
1.1.4 Study Contributions: Enhancing Well-Being in Medical Education
1.2 Methods
1.2.1 Aim and Objectives of the Study
1.2.2 Participants and Procedure
1.2.3 Ethical Consideration
1.2.4 Materials
1.2.4.1 Questionnaire for Sociodemographic Characteristics
1.2.4.2 PERMA+ Profiler Measure
1.2.5 Statistical Analyses
1.3 Results
1.4 Discussion
1.5 Limitations and Future Directions
1.5.1 Limitations
1.5.2 Future Directions
1.6 Conclusion
References
2. Deep Generative AI for Psychopathology Classification and Children’s Handwriting Recognition
Bintul Huda
2.1 Introduction
2.2 Generative AI: An Overview
2.2.1 Core Principles of Generative AI
2.2.2 Primary Architectures in Generative AI
2.3 Psychopathology Classification Using Deep Generative AI
2.3.1 Psychopathology Classification
2.3.2 The Role of Generative AI
2.3.3 Case Studies
2.3.4 Ethical Considerations
2.3.5 Shortcomings of Conventional Approaches to Classification
2.4 Children’s HWR Using Deep Generative AI
2.4.1 Problem Statement
2.4.2 Objectives
2.4.3 Methodology
2.4.3.1 Tools
2.4.3.2 Detailed Methodology
2.4.3.3 Research Plan Schedule
2.4.3.4 Scope of the Study
2.4.3.5 Future Scope
2.4.4 Expected Outcomes
2.4.5 Novelty
2.5 Limitations and Future Directions
2.5.1 Common Constraints
2.5.2 Future Research
2.6 Conclusion
References
3. AI-Driven Drug Discovery and Personalized Medicine in Cancer
Suresh Bhosale and Vibha Vyas
3.1 Introduction
3.2 AI in Drug Discovery for Cancer
3.2.1 AI in Target Identification and Validation
3.2.2 AI in Drug Design
3.2.3 AI in Drug Repurposing
3.3 Personalized Medicine in Cancer
3.3.1 Genomic Profiling and Tumor Sequencing
3.3.2 AI in Biomarker Discovery
3.3.3 Anticipated Outcomes of the Treatment
3.3.4 AI in Immuno-Oncology
3.4 AI in Clinical Trials
3.4.1 AI in Recruitment and Trial Design
3.4.2 The Role of AI in Clinical Trials Monitoring and Data Evaluation
3.5 Challenges and Limitations
3.5.1 The Quality and Accessibility of Data
3.5.2 Concerns about Ethics and Privacy
3.5.3 Regulatory and Validation Issues
3.6 Future Directions
3.7 Conclusion
References
4. Integration of Multiomics Data for Precision Cancer Research
Suresh Bhosale and Vibha Vyas
4.1 Introduction
4.2 The Need for Multiomics Integration
4.3 Multiomics Data Types and Their Relevance
4.4 Challenges in Multiomics Data Integration
4.4.1 Data Volume and Complexity
4.4.2 Data Heterogeneity
4.4.3 Missing Data
4.4.4 Biological Interpretation
4.4.5 Computational and Resource Demands
4.4.6 Addressing the Challenges
4.5 Computational Approaches for Multiomics Integration
4.5.1 Supervised Learning
4.5.2 Unsupervised Learning
4.5.3 Network-Based Approaches
4.5.4 Probabilistic Models
4.5.5 Multiomics-Specific Frameworks
4.6 Applications of Multiomics Integration in Cancer Research
4.7 Future Perspectives
4.8 Conclusion
References
5. Leveraging RNNs for Cancer Time-Series Data Analysis
Suresh Bhosale, Vibha Vyas, Saif M.B. Al Sabti and Raid Gaib
5.1 Introduction
5.2 Overview of RNNs and Time-Series Data
5.2.1 What are RNNs?
5.2.1.1 Key Components of RNNs
5.2.1.2 Key Variants of RNNs
5.2.2 Time-Series Data in Oncology
5.2.2.1 Characteristics of Time-Series Data in Cancer Research
5.3 Applications of RNNs in Cancer Time-Series Analysis
5.3.1 Early Detection and Diagnosis
5.3.2 Treatment Response Prediction
5.3.3 Prognostic Modeling
5.3.4 Мultimodal Data Integration
5.4 Challenges in Using RNNs for Cancer Data
5.4.1 Data Scarcity and Imbalance
5.4.2 High Dimensionality
5.4.3 Noise and Missing Data
5.4.4 Computational Complexity
5.5 Enhancing RNN Performance in Cancer Analysis
5.5.1 Transfer Learning
5.5.2 Data Augmentation
5.5.3 Feature Selection
5.5.4 Hybrid Models
5.6 Case Studies
5.6.1 Forecasting Tumor Growth for Breast Cancer
5.6.2 Survival Analytics in Lung Cancer
5.6.3 Personalized Treatment Recommendations
5.6.4 Early Detection in Colorectal Cancer
5.7 Future Directions
5.8 Conclusion
References
6. Designing Nanobots for Targeted Drug Delivery and the Application of Nanotechnology in Modern Medical Advancements
Jayant Pawar, Kalpana Malpe, Saif M.B. Al Sabti and Raid Gaib
6.1 Introduction
6.2 Related Work
6.3 Fundamentals of Nanotechnology
6.4 Related Algorithms in Nanobot Drug Delivery
6.5 Applications of Nanobots in Medicine
6.6 Challenges in Nanobot Development and Application
6.7 Results and Discussion
6.8 Conclusion
References
7. Reducing Latency in Critical Care Internet of Things Systems Using Edge Computing Advancements in Healthcare Technologies
Emmanuel Bhore, Rasika Ranjit Chafle, Saif M.B. Al Sabti and Raid Gaib
7.1 Introduction
7.2 Background and Literature Review
7.3 Edge Computing in Healthcare
7.4 Latency Reduction Techniques
7.5 Case Studies and Use Cases
7.6 Future Trends and Directions
7.7 Result and Discussion
7.8 Conclusion
References
8. Evaluating Precision of Artificial Intelligence–Driven Robotic Assistants in Complex Surgeries within Robotic Surgery Healthcare Advancements
Sweta Colvin and Vibha Vyas
8.1 Introduction
8.2 Background Work
8.3 Evolution of Robotic Surgery
8.4 Methodology
8.5 AI and Robotics in Complex Surgeries
8.6 Future Prospects and Innovations
8.7 Result and Discussion
8.8 Conclusion
References
9. Enhancing Cancer Diagnosis through Artificial Intelligence–Assisted Histopathology in Digital Pathology Applications for Healthcare Management
Anand Gudur, Pawar Atul Namdev, Saif M.B. Al Sabti and Raid Gaib
9.1 Introduction
9.2 Fundamentals of Histopathology
9.3 Related Algorithms for AI-Assisted Histopathology
9.4 Digital Pathology and Its Integration into Healthcare
9.5 Artificial Intelligence in Histopathology
9.6 Result and Discussion
9.7 Conclusion
References
10. Developing Smart Hydrogels for Wound Care and Drug Delivery Applications with Hydrogel Technology Advancements in Healthcare Systems
Asma A. Hussain, Garagate Amruta K., Saif M.B. Al Sabti and Raid Gaib
10.1 Introduction
10.2 Background and Literature Review
10.3 Methodology
10.4 Smart Hydrogels and Their Mechanisms
10.5 Smart Hydrogels in Wound Care
10.6 Results and Discussion
10.7 Conclusion
References
11. AI-Enhanced Image Analysis for Chronic Disease Diagnosis: AI-Driven Advancements in Medical Imaging (MRI, CT, X-Rays) for Diagnosing Chronic Diseases Like Cancer and Liver Cirrhosis
Prakash Patil and Rasika Ranjit Chafle
11.1 Introduction
11.2 Overview of Chronic Diseases and Diagnostic Challenges
11.3 AI in Medical Imaging
11.4 Applications of AI-Enhanced Image Analysis in Chronic Disease Diagnosis
11.5 Benefits of AI-Enhanced Image Analysis
11.6 Challenges and Limitations of AI in Chronic Disease Diagnosis
11.7 Conclusion
References
12. AI in Chronic Kidney Disease: Monitoring, Prediction, and Prevention How AI Helps Track Kidney Function, Predict Progression, and Optimize Dialysis Treatments
Anil Huddedar and K. Gavhale
12.1 Introduction
12.2 AI in Monitoring Kidney Function
12.3 Predictive Analytics in CKD
12.4 AI-Driven Dialysis Optimization
12.5 Preventive Strategies Using AI
12.6 Ethical Considerations and Patient Privacy
12.7 Future Directions and Innovations
12.8 Conclusion
References
13. Advanced Survival Analysis for Cancer Prognostics Using AI
Sujata Sanjay Kumbhar
13.1 Introduction
13.2 Traditional Survival Analysis Methods
13.2.1 The Kaplan–Meier System
13.2.2 Model of Cox Proportional Hazards
13.2.3 Limitations of Traditional Methods
13.3 AI-Driven Survival Analysis
13.3.1 Survival Analysis Using ML
13.3.1.1 Forests of Random Survival
13.3.1.2 SVMS for Survival Analysis
13.3.2 Survival Analysis Using DL
13.3.2.1 DeepSurv
13.3.2.2 Time-to-Event Prediction Using RNNs
13.3.3 Combining Data from Multiple Omics
13.4 Applications in Cancer Prognostics
13.4.1 Risk Stratification
13.4.2 Customized Treatment Strategy
13.4.3 Early Detection of Recurrence
13.5 Difficulties and Prospects
13.5.1 Availability and Quality of Data
13.5.2 Interpretability
13.5.3 Integration with Clinical Workflows
13.5.4 Moral Points to Remember
13.5.5 Future Directions
13.6 Illustrative Examples
13.6.1 Case Study 1: Prognosis of Breast Cancer
13.6.2 Case Study 2: AI-Driven Prognostics in Lung Cancer
13.6.3 Case Study 3: Personalized Treatment Strategies in Colorectal Cancer
13.6.3.1 AI-Driven Personalization in Treatment Planning
13.6.3.2 Effect on Medical Results
13.7 Conclusion
References
14. AI-Driven Innovations in Cancer Screening and Detection
N. J. Patil
14.1 Introduction
14.2 Overview of AI in Medical Diagnostics
14.2.1 Machine Learning
14.2.2 Deep Learning
14.2.3 Natural Language Processing
14.3 Using AI to Screen for Cancer
14.3.1 AI in Imaging-Based Detection
14.3.2 Liquid Biopsies and Biomarker Detection
14.3.3 Histopathology
14.4 Real-World Implementations
14.4.1 AI-Assisted Lung Cancer Screening
14.4.2 Colorectal Cancer Detection
14.4.3 Skin Cancer Identification
14.5 Key Innovations
14.5.1 Federated Learning
14.5.2 Explainable AI
14.5.3 Multiomics Integration
14.6 Challenges in Implementation
14.6.1 Data Quality and Bias
14.6.2 Regulations and Standards
14.6.3 Incorporation into Clinical Workflows
14.7 Prospects for the Future
14.8 Conclusion
References
15. Exploring Transfer Learning for Improved Cancer Diagnostics
Avinash Mane
15.1 Introduction
15.2 Theoretical Foundations of TL
15.2.1 What is TL?
15.2.2 TL Types
15.2.3 Feature Representations and Pretrained Models
15.2.4 Feature Representation and Model Adaptation
15.3 Applications in Cancer Diagnostics
15.3.1 Diagnostics Based on Imaging Access
15.3.2 Molecular and Genetic Data
15.3.3 Multimodal Diagnostics
15.4 Challenges in TL for Cancer Diagnostics
15.5 Future Directions
15.6 Conclusion
References
16. Integrating AI and Multiomics for Breakthroughs in Cancer Studies
Atul Bhanudas Hulwan
16.1 Introduction
16.2 Multiomics in Cancer Research
16.3 AI Techniques in Multiomics Analysis
16.4 Cancer Research Applications of AI and Multiomics
16.4.1 Early Cancer Detection
16.4.2 Personalized Medicine
16.4.3 Biomarker Discovery
16.4.4 Drug Development
16.4.5 AI and Immuno-Oncology
16.4.6 AI in Radiogenomics and Image Analysis
16.4.7 AI in Clinical Decision Support Systems
16.4.8 AI in Cancer Epidemiology and Public Health
16.5 Challenges in AI and Multiomics Integration
16.5.1 Data Heterogeneity
16.5.2 Computational Complexity
16.5.3 Interpretability
16.5.4 Ethical Concerns
16.6 Future Directions
16.7 Conclusion
References
17. Model Evaluation and Validation Strategies in Cancer Research
Anand Gudur
17.1 Introduction
17.2 Key Concepts in Model Evaluation and Validation
17.2.1 Definition and Scope
17.2.2 Importance in Cancer Research
17.2.3 Key Benefits
17.3 Strategies for Model Evaluation
17.3.1 Performance Metrics
17.3.2 Methods of Cross-Validation
17.3.3 Bootstrapping
17.3.4 Independent Test Sets
17.4 Validation Strategies
17.5 Challenges in Model Evaluation and Validation
17.6 Innovations in Model Evaluation and Validation
17.7 Best Practices for Model Evaluation and Validation in Cancer Research
17.8 Conclusion
References
18. Multiomics Data Integration: Transforming Cancer Research
Sujata Raghunath Kanetkar
18.1 Introduction
18.1.1 Challenge of Cancer Therapy
18.1.2 The Emergence of Multiomics Approaches
18.1.3 Paper Goals
18.2 Techniques for Integrating Multiomics Data
18.2.1 Data Cleaning and Adjusting to Uniform Scale
18.2.2 Combining Various Datasets: Methods Available
18.2.2.1 Concatenation-Based Methods
18.2.2.2 Methods Based on Transformation
18.2.2.3 Model-Based Approaches
18.2.3 Network-Based Integration
18.2.4 The Two Emerging Fields of Study: Artificial Intelligence and ML
18.2.4.1 Supervised Learning
18.2.4.2 Unsupervised Learning
18.2.4.3 Deep Learning
18.3 Challenges in Multiomics Data Integration
18.3.1 Data Heterogeneity
18.3.2 Data Dimensionality
18.3.3 Missing Data
18.3.4 Biological Interpretation
18.3.5 Computational and Statistical Challenges
18.4 Multiomics Data Integration Applications in Cancer Research
18.4.1 Discovery of New Biomarkers
18.4.2 Explanation of Molecular Pathways
18.4.3 Tailored Images of Cancer Therapy
18.4.4 Drug Repurposing and Discovery
18.4.5 Understanding Tumor Heterogeneity
18.4.6 Creation of a Prognostic Model
18.4.7 Immuno-Oncology and Tumor Immune Profiling
18.5 Case Studies in Multiomics Data Integration
18.5.1 The Cancer Genome Atlas
18.5.2 The International Cancer Genome Consortium
18.5.3 The Consortium for Clinical Proteomic Tumor Analysis
18.5.4 The Encyclopedia of Cancer Cell Lines
18.6 Future Directions in Multiomics Data Integration
18.6.1 New Directions in Thematic Studies
18.6.2 Integration of Multiomics and Clinical Data
18.6.3 Development of Standardized Protocols and Tools
18.6.4 Ethical and Privacy Concerns
18.6.5 The Function of AI
18.7 Conclusion
References
19. RNN Applications in Longitudinal Cancer Data Analysis
Rashmi Gudur
19.1 Introduction
19.1.1 Context
19.1.2 The Role of RNNs in Longitudinal Data Analysis
19.1.3 Aims and Goals of the Project
19.2 RNNs: Architecture and Variants
19.2.1 Basic RNN Architecture
19.2.2 Networks with LSTM
19.2.3 Gated Recurrent Units
19.2.4 Bidirectional RNNs
19.2.5 Attention Mechanisms
19.3 Applications of RNNs in Longitudinal Cancer Data Analysis
19.3.1 Modeling Cancer Progression
19.3.2 Predicting Treatment Outcomes
19.3.3 Addressing Incomplete Information
19.3.4 Identifying Biomarkers
19.3.5 Personalized Medicine
19.4 Case Studies
19.4.1 Case Study 1: Predicting Breast Cancer Recurrence
19.4.2 Case Study 2: Modeling Lung Cancer Progression
19.4.3 Case Study 3: Identifying Biomarkers for Colorectal Cancer
19.4.4 Case Study 4: Predicting Chemotherapy Toxicity in Leukemia Patients
19.5 Advantages and Limitations of RNNs in Longitudinal Cancer Data Analysis
19.5.1 Advantages
19.5.2 Limitations
19.6 Future Directions
19.7 Conclusion
References
20. The Role of Transfer Learning in Accelerating Cancer Research
Kailas Datkhile
20.1 Introduction
20.2 The Basics of Transfer Education
20.2.1 Definition and Key Concepts
20.2.2 Transfer Learning Types
20.3 Use of Transfer Learning in Research on Cancer
20.3.1 Biomarker Discovery
20.3.2 Drug Discovery
20.3.3 Treatment Personalization
20.3.4 Imaging and Histopathology
20.4 Transfer Learning’s Advantages for Cancer Research
20.4.1 Changes and Adapting
20.4.2 Efficiency and Cost
20.4.3 Generalizability
20.4.4 Enhanced Cancer Research Accuracy
20.5 Challenges and Limitations
20.5.1 Data Heterogeneity
20.5.2 Domain Adaptation
20.5.3 Interpretability
20.5.4 Ethical and Legal Issues
20.6 Prospects for the Future
20.6.1 Federated Learning
20.6.2 Deriving Synthetic Data
20.6.3 AI That can be Explained
20.6.4 Multimodal Learning
20.7 Conclusion
References
21. Pioneering Innovations in Healthcare and Energy: A Deep Generative AI Approach to Early Psychopathology Prediction and Adaptive Series Algorithm Management for Green Hydrogen Market Analysis
Ibrahim Kadriinamdar, Manisha Paliwal, Alamgir Sani, Kumari Lipi and Madhuranjan Vatsa
21.1 Introduction
21.2 AI in Early Psychopathology Prediction
21.2.1 Deep Generative AI Models
21.2.2 Natural Language Processing in Psychopathology
21.2.3 Monitoring Behavior with AI
21.2.4 Applications and Benefits
21.3 Adaptive Series Algorithm Management in Green Hydrogen Market Analysis
21.3.1 Green Hydrogen: A Key to Sustainable Energy
21.3.2 Adaptive Series Algorithm Approach
21.3.3 Case Study: Gujarat’s Green Hydrogen Market
21.4 Converging AI in Healthcare and Energy
21.4.1 The Collective Advantages of AI in Healthcare and Energy Sectors
21.4.2 AI-Driven Innovations Bridging Healthcare and Energy
21.4.3 Future Prospects and Collaborative AI Strategies
21.5 Challenges and Future Directions
21.6 Conclusion
References
22. Building the Foundation: Machine Learning’s Impact on Mental Health Diagnostics
K. Palani, Jothikumar R., M. Nagarajan, E. Sivarajan, S. Sathya and Gouri M.S.
22.1 Introduction
22.1.1 Increasing Need for More Accurate Mental Illness Diagnoses
22.1.2 Role of ML in Mental Health
22.1.3 Our Approach: SG-BRT
22.1.4 Data and Features for Mental Health Diagnostics
22.1.5 Challenges of Mental Health Data
22.1.6 Contribution of SG-BRT to Mental Health Diagnostics
22.2 Literature Review
22.3 Methodology
22.3.1 Data Collection and Preprocessing
22.3.1.1 Data Source
22.3.1.2 Data Cleaning
22.3.1.3 Feature Engineering
22.3.2 Handling Class Imbalance
22.3.3 Model Design
22.3.3.1 RF Model
22.3.3.2 GB Model
22.3.3.3 Stacking the Models
22.3.4 Model Training and Validation
22.3.4.1 Evaluation Metrics
22.3.5 Feature Importance Analysis
22.3.5.1 Top Features for Depression Diagnosis
22.3.5.2 Top Features for Anxiety Diagnosis
22.4 Results
22.4.1 Summary of Results
22.5 Conclusion
References
23. Ensemble Learning Approaches to Enhance Personalized Medicine in Treatment Planning
K. Palani, Jothikumar R., Sherin Eliyas, E. Sivarajan, M. Nagarajan and Gouri M. S.
23.1 Introduction
23.1.1 Background
23.1.2 Ensemble Learning Significance
23.1.3 Objectives
23.1.4 Significance of the Study
23.2 Literature Review
23.3 Methodology
23.3.1 Objective
23.3.2 Data Collection
23.3.3 Preprocessing
23.3.4 Feature Selection
23.3.5 Ensemble Learning Techniques
23.3.6 Model Evaluation Metrics
23.3.7 Cross-Validation
23.4 Results
23.4.1 Model Performance
23.5 Feature Importance
23.6 Results of Model Comparison
23.6.1 Cross-Validation Results
23.6.2 Model Comparison
23.7 Conclusion
References
24. Ensemble Learning in Psychopathology: A New Era of Predictive Analysis
Susi S., Mohammed Waheeduddin Hussain, Sathish Kumar M., M. Ravichandran, E. Sivarajan, M. Nagarajan and Jayendra Kumar
24.1 Introduction
24.1.1 Background
24.1.2 Importance of Predictive Analysis in Psychopathology
24.1.3 Role of Ensemble Learning
24.1.4 Ensemble Techniques of Learning
24.1.5 Objectives of the Study
24.1.6 Dataset Overview
24.1.7 Overview of Methodology
24.1.8 Implication of the Study
24.2 Literature Review
24.3 Methodology
24.3.1 Introduction
24.3.2 Data Collection
24.3.2.1 Dataset Description
24.3.3 Data Preprocessing
24.3.3.1 Management of Missing Values
24.3.3.2 Data Normalization
24.3.3.3 Encoding Categorical Variables
24.3.4 Model Selection
24.3.4.1 Random Forest
24.3.4.2 Gradient Boosting
24.3.4.3 Voting Classifier
24.3.5 Training and Evaluation of Models
24.3.5.1 Train the Models
24.3.5.2 Performance Measures
24.4 Results
24.4.1 Results Analysis
24.4.2 Confusion Matrix
24.4.3 Feature Importance Analysis
24.4.4 Feature Importance Scores
24.4.5 Implications from Feature Importance
24.5 Discussion of Results
24.5.1 Overall Model Performance
24.5.2 Clinical Implications
24.5.3 Limitations and Future Research
24.6 Conclusion
References
25. Applications of AI in Early Diagnosis of Neurodevelopmental Disabilities: A Deep Learning Approach
Durai Vasanth R., S. Varadharajan, Suganya. K., Harishchander Anandaram, Shreenidhi K.S. and B. Prameela Rani
25.1 Introduction
25.1.1 The Importance of Early Diagnosis in Neurodevelopmental Disabilities
25.1.2 Challenges in Traditional Diagnosis Approaches
25.1.3 The Emergence of AI in Healthcare
25.1.4 Deep Learning: A Subset of AI Revolutionizing Diagnosis
25.1.5 Applications of AI in Neurodevelopment Disabilities
25.1.6 The Potential of Scalable and Accessible Solutions
25.1.7 Transforming Lives through AI-Driven Early Diagnosis
25.2 Literature Review
25.3 Methodology
25.3.1 Data Collection and Preprocessing
25.3.2 Data Preprocessing
25.3.3 Design Deep Learning Models
25.3.4 Model Evaluation and Validation
25.3.5 Ethical Considerations and Bias Mitigation
25.3.6 Deployment and Implementation
25.3.7 Impact Assessment
25.4 Results
25.4.1 Data Collection and Sources
25.4.2 Data Preprocessing Techniques
25.4.3 Deep Learning Model Designs
25.4.4 Model Evolution and Validation
25.5 Conclusion
References
26. Advancing Neuroimaging with Deep Learning: Principles, Techniques and Applications
S. Murugaanandam, N. Elamathi, Harishchander Anandaram, Shreenidhi K. S., Priya V. and B. Prameela Rani
26.1 Introduction
26.1.1 The Evolution of Neuroimaging
26.1.2 Deep Learning in Neuroimaging
26.1.3 Applications Revolutionizing Neuroscience and Medicine
26.1.4 A Vision for the Future
26.1.5 The Role of Big Data in Neuroimaging
26.1.6 Personalized Medicine and Treatment Planning
26.1.7 Overcoming Challenges in Data Interpretation and Generalization
26.2 Literature Review
26.3 Methodology
26.3.1 Literature Review and Conceptual Framework
26.3.2 Data Acquisition and Preprocessing
26.3.3 Deep Learning Model Development
26.3.4 Validation and Evaluation
26.3.5 Application and Case Studies
26.3.6 Ethical Considerations and Challenges
26.4 Results
26.4.1 Early Detection of Alzheimer Disease
26.4.2 Brain Tumor Segmentation
26.4.3 Financial Connectivity Analysis
26.4.4 Personalized Treatment Prediction for Stroke Recovery
26.5 Conclusion
References
27. Managing and Analyzing Large Neurological Datasets: Challenges and AI-Driven Solutions
V. Ceronmani Sharmila, M. Jenath, V. Sumitra, S. Sunithamani, J. Abanah Shirley
and S. N. Lakshmi Malluvalasa
27.1 Introduction
27.1.1 The Emergence of Big Data in Neuroscience
27.1.2 AI as Revolutionary Tool for Neuroscience
27.1.3 Challenges to the Application of AI in Neurological Data
27.1.4 Future Directions for AI and Neuroscience
27.1.5 Ethics in AI Neuroscience
27.1.6 The Role of Interdisciplinary Collaboration
27.2 Literature Review
27.3 Methodology
27.3.1 Literature Review
27.3.2 Dataset Analysis and Classification
27.3.3 Identification of AI Techniques
27.3.4 AI Applications Case Studies
27.3.5 Ethical and Practical Considerations
27.3.6 Framework of Future Directions
27.4 Results
27.4.1 Task and Accuracy in Literature Review
27.4.2 Dataset Analysis and Categorization
27.4.3 Identification of AI Techniques
27.4.4 Case Studies of AI Techniques of Neuroscience
27.5 Conclusion
References
28. AI-Powered Mental Healthcare: Investigating the Role of Generative Deep Learning Models in Personalized Treatment and Cognitive Behavioral Therapy
R. Prasanna, M. Jenath, D. Tharani, Lakshmi Thara R., Arumbu V. N. and Sankar Ganesh Karuppasamy
28.1 Introduction
28.1.1 Background
28.1.2 Objectives
28.1.3 Scope
28.1.4 Significance
28.1.5 Challenges and Limitations
28.1.6 Future Directions
28.2 Literature Review
28.3 Methodology
28.3.1 Research Design
28.3.2 Data Collection
28.3.2.1 Sources of Data
28.3.2.2 Data Preprocessing
28.3.3 Model Development
28.3.3.1 Generative Deep Learning Model Selection
28.3.3.2 Fine-Tuning and Training
28.3.3.3 Multimodal Integration
28.3.4 Evaluation Metrics
28.3.4.1 Quantitative Metrics
28.3.4.2 Qualitative Metrics
28.3.5 Implementation and Testing
28.3.5.1 Pilot Study
28.3.5.2 Real-World Deployment
28.3.6 Ethical and Regulatory Considerations
28.3.7 Analysis and Interpretation
28.3.8 Validation and Iteration
28.3.9 Reporting and Dissemination
28.4 Results
28.4.1 Effectiveness of AI in Personalized Treatment
28.4.2 AI-Generated CBT Efficiency
28.4.3 Comparison of Generative AI versus Traditional Methods in Therapy
28.4.4 Patient Experience and Ethical Considerations in AI-Powered Therapy
28.5 Discussion of Results
28.6 Conclusion
References
29. Mental Illness with Deep Learning
Kumud Sachdeva and Ayush Mahanta
29.1 Introduction
29.1.1 Understanding Mental Illness in the Modern Age
29.1.2 Socioeconomic Burden and Global Inequities
29.1.3 The Role of Artificial Intelligence and Deep Learning in Psychiatry
29.1.4 Gaps in Traditional Mental Health Approaches
29.1.5 Motivation for AI Integration in Mental Healthcare
29.1.6 Structure and Scope of the Chapter
29.2 Deep Learning Fundamentals for Mental Health
29.2.1 Neural Networks in Mental Health AI
29.2.2 Convolutional Neural Networks
29.2.3 RNNs and Long Short-Term Memory
29.2.4 Transformers and Self-Attention Models
29.2.5 Explainable AI in Clinical Settings
29.2.6 Comparative Summary and Model Suitability
29.3 Mental Health Datasets and Feature Engineering
29.3.1 EHRs and International Classification of Diseases, Tenth Revision–Coded Diagnoses
29.3.2 Social Media Data (Reddit, Twitter, Facebook)
29.3.3 Neuroimaging Datasets (MRI, fMRI, Positron Emission Tomography, EEG)
29.3.4 Voice, Speech, and Facial Expression Data
29.3.5 Sensor Data from Smartphones and Wearables
29.3.6 Data Labeling, Annotation Bias, and Ground-Truth Issues
29.4 Clinical Applications of Deep Learning in Mental Illness
29.4.1 Depression Detection
29.4.2 Anxiety Classification
29.4.3 Bipolar Disorder and Mood Tracking
29.4.4 Schizophrenia Prediction
29.4.5 Suicide Risk Detection
29.5 AI for Treatment, Intervention, and Monitoring
29.5.1 Woebot: Conversational AI for Depression
29.5.2 MindStrong: Mobile Sensor Data for Cognitive Health
29.5.3 MIT Media Lab Suicide Prediction Project
29.6 Challenges and Limitations in Deep Learning for Mental Health
29.6.1 Data Scarcity, Quality, and Imbalance
29.6.2 Overfitting and Generalization in Small Datasets
29.6.3 Explainability in Clinical Settings
29.6.4 Model Drift and Continuous Learning Needs
29.6.5 Cultural, Age, and Linguistic Biases
29.6.6 Integration with Medical Systems (HL7, FHIR)
29.7 Ethical, Legal, and Social Implications
29.7.1 Algorithmic Fairness and Bias
29.7.2 Digital Consent and Patient Autonomy
29.7.3 Transparency and the “Right to Explanation”
29.7.4 Societal Impacts and Misuse of AI in Psychiatry
29.7.5 Toward Responsible Innovation
29.8 Future Directions in Deep Learning for Mental Health
29.8.1 Multimodal AI and Holistic Diagnosis
29.8.2 FL for Privacy-Preserving Training
29.8.3 Emotion-Aware and Empathetic AI
29.8.4 BCIs in Psychiatry
29.8.5 Edge AI for Real-Time, Offline Mental Health Support
29.9 Conclusion
References
30. Psychopathology and AI in Mental Health
Priya Batta and Arjun Singh
Introduction
Literature Review
Methodology
Results and Discussion
Conclusion and Future Scope
References
31. Generative AI Models for Mental Health Diagnosis and Therapy: A Comparative Analysis of GANs, VAEs, and Transformer-Based Approaches
M. Jenath, V. Sumitra, V. Ceronmani Sharmila, J. Abanah Shirley, S. Sunithamani and T. Sajana
31.1 Introduction
31.1.1 Background
31.1.2 Objectives
31.1.3 Scope
31.1.4 Significance
31.1.5 Challenges and Limitations
31.1.6 Future Directions
31.2 Literature Review
31.3 Methodology
31.3.1 Data Preprocessing and Collection
31.3.2 Training and Validation
31.3.3 Evaluation Metrics
31.4 Results
31.4.1 Synthetic Data Creation
31.4.2 Automated Diagnosis
31.4.3 AI-Assisted Therapy
31.4.4 Comparative Analysis of Models
31.4.5 Hybrid Model Potential
31.5 Discussion of Results
31.6 Conclusion
References
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Psychopathology Early Prediction and Classification