1. Introduction
  1.1 Significance of predictive modeling in healthcare diagnostics
  1.2 Research objectives
2. Literature review
  2.1 Overview of predictive modeling in healthcare
  2.2 Applications of machine learning in disease diagnosis
  2.3 Challenges and opportunities in healthcare predictive analytics
3. Data collection and preprocessing
  3.1 Selection of healthcare datasets and variables
  3.2 Data cleaning and feature engineering
  3.3 Ethical considerations and patient privacy
4. Predictive model development
  4.1 Selection of machine learning algorithms
  4.2 Model training and validation
  4.3 Performance metrics and evaluation criteria
5. Case studies and experiments
  5.1 Application of predictive models to specific diseases
  5.2 Comparative analysis with traditional diagnostic methods