Cardiovascular Risk Predictor

This project is a web application for predicting cardiovascular disease risk using a machine learning model. Users can input their health data and receive a risk prediction, as well as view and manage previous prediction results.

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Features

• Predict cardiovascular risk based on user input (age, gender, blood pressure, cholesterol, etc.)
• View previous prediction results (last 10)
• Delete selected or all previous records
• Responsive and clean UI
• Data stored in CSV for easy access
• Jupyter Notebook included for model training and exploration

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Limitations

• Predictions are only as accurate as the data and model used; not a substitute for professional medical advice.
• Only supports the features present in the form; cannot handle missing or additional data.
• Model and preprocessor files (cardio_risk_model.pkl, cardio_preprocessor.pkl) must be present in the project root.
• No user authentication; all users share the same prediction history.
• Data is stored in a CSV file, which is not suitable for production or multi-user environments.
• The web app is intended for educational/demo purposes only.

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Project Structure

cardio_project/
│
├── app/
│   ├── app.py                # Flask application
│   ├── static/
│   │   └── style.css         # CSS styles
│   └── templates/
│       └── index.html        # Main HTML template
│
├── cardio_risk_model.pkl     # Trained ML model
├── cardio_preprocessor.pkl   # Preprocessing pipeline
├── cardio_project.ipynb      # Jupyter Notebook for model training and exploration
├── cardio_train.csv          # Training data (optional)
├── requirements.txt          # Python dependencies

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Installation

1. Clone the repository

   git clone machinelearning-cardio-project
   cd cardio_project

2. Install dependencies

   pip install -r requirements.txt

3. Ensure model files exist

   Place cardio_risk_model.pkl and cardio_preprocessor.pkl in the project root.

4. Run the application

   cd app
   python app.py

5. Open in browser

   Visit http://localhost:5000

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Running the Jupyter Notebook

1. Install Jupyter if not already installed

   pip install notebook

2. Start Jupyter Notebook

   jupyter notebook

3. Open and run cardio_project.ipynb
   Use this notebook to explore data, train, and export your own models.

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Usage

• Fill in your health data and submit the form to get a risk prediction.
• View your last 10 predictions in the "Previous Results" panel.
• Use the "Delete Selected" or "Clear All" buttons to manage your records.

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License

© 2025 noamanayub | This Project is Open Source and Educational Purpose.