1. Introduction
  - Healthcare Analytics and Predictive Modeling
  - Objectives and Significance of the Project
2. Literature Review
  - Machine Learning in Healthcare
  - Predictive Analytics for Disease Diagnosis
  - Clinical Decision Support Systems
3. System Architecture and Design
  - Data Collection and Preprocessing
  - Feature Selection and Model Training
  - Model Evaluation and Validation
4. Implementation Details
  - Machine Learning Framework Selection
  - Algorithm Development and Optimization
  - Integration with Healthcare Information Systems
5. Evaluation and Testing
  - Performance Metrics and Validation Methods
  - Clinical Utility and Impact Assessment
  - Ethical and Regulatory Considerations