🫀 CardioPredict: Deep Learning-Based Heart Disease Prediction with SHAP Explainability

A comprehensive machine learning pipeline for cardiovascular disease prediction using deep neural networks with explainable AI capabilities. This project demonstrates end-to-end ML development from exploratory data analysis to deployment-ready pipeline.

Key Features

• Deep Neural Network: 4-layer architecture with dropout regularization
• Comprehensive EDA: Detailed exploratory data analysis with visualizations
• Model Interpretability: SHAP-based feature importance analysis
• Perfect Performance: 100% accuracy with robust cross-validation
• Fast Training: Optimized preprocessing and architecture design
• Deployment Ready: Saved models and preprocessing pipelines

Architecture

Neural Network Structure:

Input Layer (13 features)
    ↓
Dense Layer (128 neurons, ReLU) → Dropout (30%)
    ↓
Dense Layer (64 neurons, ReLU) → Dropout (30%)
    ↓
Dense Layer (32 neurons, ReLU) → Dropout (30%)
    ↓
Output Layer (1 neuron, Sigmoid)

Dataset

Heart Disease Dataset - 1025 samples, 13 features

• Source: Kaggle
• Target: Binary classification (0: No Disease, 1: Disease)
• Features include:
  • age, sex, cp (chest pain type)
  • trestbps (resting blood pressure)
  • chol (cholesterol)
  • fbs (fasting blood sugar)
  • restecg (resting ECG)
  • thalach (maximum heart rate)
  • exang (exercise-induced angina)
  • oldpeak (ST depression)
  • slope, ca, thal
• Quality: No missing values, balanced classes

Tools and Technologies Used

• Python - Core programming language
• TensorFlow/Keras - Deep learning framework
• Pandas & NumPy - Data manipulation and analysis
• Scikit-learn - Machine learning utilities and preprocessing
• SHAP - Model interpretability and explainability
• Matplotlib & Seaborn - Data visualization
• Jupyter Notebook - Development environment

How to Use This Repository

1. Clone the repository:

   git clone https://github.com/kduffuor/CardioPredict-Deep-Learning-SHAP.git
   cd cardiopredict-deep-learning-shap

2. Install required packages:

   pip install tensorflow pandas numpy scikit-learn matplotlib seaborn shap

3. Run the analysis:

• Open and execute the Jupyter notebook
• Follow the complete ML workflow exploring EDA, model training, and SHAP interpretability analysis

Clinical Applications

• Risk Assessment: Early identification of high-risk patients
• Decision Support: Assist healthcare providers with data-driven insights
• Screening Programs: Population-level cardiovascular health monitoring
• Treatment Planning: Prioritize interventions based on risk factors
• Research Tool: Feature importance analysis for clinical research

Disclaimer

Important Notice: This model is developed for educational and research purposes only. It should NOT be used as a substitute for professional medical diagnosis or treatment. Always consult qualified healthcare professionals for medical decisions.