Evaluating the Impact of Explainable AI Techniques on Health Disease Prediction Models' Accuracy

1. Introduction 1.1 Background and Motivation 1.2 Objectives of the Study 1.3 Structure of the Thesis 2. Overview of Health Disease Prediction Models 2.1 Commonly Used Models 2.2 Limitations and Challenges 2.3 Importance of Accuracy 3. Explainable AI Techniques 3.1 Definition and Framework 3.2 Popular Explainability Methods 3.3 Role of Explainability in AI 4. Integration of Explainable AI in Prediction Models 4.1 Methodologies and Approaches 4.2 Case Studies and Examples 4.3 Challenges in Integration 5. Impact on Prediction Models' Accuracy 5.1 Evaluation Metrics and Criteria 5.2 Comparative Analysis 5.3 Statistical Analysis Results 6. Case Study: Application in Health Sector 6.1 Selected Health Conditions 6.2 Data Collection and Preprocessing 6.3 Implementation and Findings 7. Discussion and Interpretation 7.1 Implications of Findings 7.2 Limitations of Study 7.3 Future Research Directions 8. Conclusion 8.1 Summary of Key Insights 8.2 Contributions to the Field 8.3 Final Thoughts and Recommendations