Cardiovascular Disease Prediction System Using Ensemble Machine Learning

📌 Overview

This project is an advanced Cardiovascular Disease Prediction System built with Streamlit and Machine Learning. It utilizes an Ensemble Stacking Classifier (combining Random Forest, XGBoost, etc.) to predict the likelihood of heart disease based on patient medical attributes.

The application provides a user-friendly interface for healthcare professionals to:

• Register and manage patients.
• Input clinical data (vitals, lab results).
• Get real-time risk assessments with detailed explanations.
• View comprehensive reports and visualizations.
• Track patient history over time.

🚀 Features

• Ensemble Machine Learning Model: High-accuracy prediction using a stacked ensemble of multiple classifiers.
• Interactive Web Interface: Built with Streamlit for easy usage.
• Patient Management System: Database integration (SQLite) to save and retrieve patient records.
• Detailed Risk Reports: Generates comprehensive reports explaining why a prediction was made, including contributing risk factors.
• Visualizations: Interactive charts for health metrics and risk analysis.
• Clinical Interpretations: Provides context for medical values (e.g., "Stage 2 Hypertension", "High Cholesterol").

📂 Project Structure

├── streamlit_app_enhanced.py    # Main Streamlit application
├── train_ensemble_model.py      # Script to train the ML model
├── patient_database.py          # Database management (SQLite)
├── FINAL_DATASET.csv            # Dataset used for training
├── requirements.txt             # Python dependencies
├── install_dependencies.bat     # Helper script for installation (Windows)
├── run_streamlit_app.bat        # Helper script to run the app (Windows)
└── models/                      # Directory where trained models are saved (.pkl files)

📊 Dataset

The project uses a comprehensive heart disease dataset (FINAL_DATASET.csv) containing the following clinical features:

• Age: Age in years.
• Sex: (1 = male; 0 = female).
• Chest Pain Type: (1 = typical angina; 2 = atypical angina; 3 = non-anginal pain; 4 = asymptomatic).
• Resting Blood Pressure: (in mm Hg).
• Cholesterol: Serum cholestoral in mg/dl.
• Fasting Blood Sugar: (1 = true; 0 = false).
• Resting ECG: (0 = normal; 1 = ST-T wave abnormality; 2 = LV hypertrophy).
• Max Heart Rate: Maximum heart rate achieved.
• Exercise Angina: (1 = yes; 0 = no).
• Oldpeak: ST depression induced by exercise relative to rest.
• ST Slope: The slope of the peak exercise ST segment.
• Target: Diagnosis of heart disease (1 = disease, 0 = no disease).

🛠️ Installation & Setup

Prerequisites

• Python 3.8 or higher
• Git

Steps

1. Clone the Repository

   git clone https://github.com/Susreel7/ensembleCardio.git
   cd ensembleCardio/Cardiovascular-Disease-Prediction-System-Using-Ensemble-Machine-Learning

2. Install Dependencies You can use the provided batch file (Windows) or pip:

   pip install -r requirements.txt

   Alternatively, run install_dependencies.bat on Windows.

3. Train the Model Before running the app, ensure the model is trained and saved:

   python train_ensemble_model.py

   This will generate ensemble_model.pkl and other necessary artifacts.

🏃‍♂️ Execution

To launch the application:

streamlit run streamlit_app_enhanced.py

Alternatively, double-click run_streamlit_app.bat on Windows.

The application will open in your default web browser at http://localhost:8501.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📜 License

This project is open source and available under the MIT License.