Heart Disease Diagnosis Project

🫀 How to Use: Enter patient features → Run prediction → View ensemble results!

📋 Notes

• Models are trained at launch on data/cleveland.csv with customizable train/validation split (default 80/20).
• Target is binarized automatically (0 = no disease, >0 = disease).
• Retrain functionality: Adjust the split ratio and click "🔄 Retrain Models" to see how data size affects performance.
• Seven optimized models are compared: Decision Tree, k-NN, Naive Bayes, Random Forest, AdaBoost, Gradient Boosting, and XGBoost.
• Hyperparameters are optimized for heart disease prediction tasks using best practices.
• Ensemble uses weighted soft voting with optimized weights based on model performance.
• Best performing model on test set is highlighted with 🏆 in the validation metrics table.
• Optimization highlights:
  • Decision Tree: entropy criterion, balanced classes, optimal depth
  • k-NN: distance weighting, Manhattan metric, optimized neighbors
  • Random Forest: 200 trees, class balancing, feature sampling
  • Gradient Boosting: regularization, subsampling, lower learning rate
  • AdaBoost: SAMME algorithm, increased estimators
  • XGBoost: L1/L2 regularization, optimal depth and learning rate
• Feature descriptions:
  • age: Patient age in years
  • sex: Gender (0=female, 1=male)
  • cp: Chest pain type (1-4)
  • trestbps: Resting blood pressure (mmHg)
  • chol: Serum cholesterol (mg/dl)
  • fbs: Fasting blood sugar >120 mg/dl (1=true, 0=false)
  • restecg: Resting ECG results (0-2)
  • thalach: Maximum heart rate achieved
  • exang: Exercise induced angina (1=yes, 0=no)
  • oldpeak: ST depression induced by exercise
  • slope: Slope of peak exercise ST segment (1-3)
  • ca: Number of major vessels colored by fluoroscopy (0-3)
  • thal: Thalassemia (3=normal, 6=fixed defect, 7=reversible defect)