❤️ Heart Disease Prediction System

A machine learning based web application that predicts the risk level of heart disease using clinical data and ECG signal analysis. The system combines multiple trained models to provide accurate risk predictions through a simple web interface.

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📌 Project Overview

Heart disease is one of the leading causes of death worldwide. Early prediction and diagnosis can help reduce risks and improve treatment outcomes.

This project uses machine learning models trained on clinical parameters and ECG signals to predict the probability of heart disease and classify patients into risk categories.

The application provides a user-friendly web interface where users can upload data and receive predictions instantly.

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🚀 Features

• Predicts heart disease risk level

• Uses both clinical data and ECG signal analysis

• Fusion model combines multiple predictions

• Web interface built with Flask

• Multiple risk categories:

  • Low Risk
  • Medium Risk
  • High Risk
  • Critical Risk

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🧠 Technologies Used

• Python
• Flask
• TensorFlow / Keras
• Scikit-Learn
• NumPy
• Joblib
• HTML / CSS

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📂 Project Structure

heart-disease-prediction
│
├── app/
│   ├── static/
│   │   └── style.css
│   │
│   ├── templates/
│   │   ├── index.html
│   │   ├── result.html
│   │   └── error.html
│   │
│   └── app.py
│
├── dataset/
│
├── models/
│   ├── fusion_model.py
│   ├── train_clinical_model.py
│   └── train_ecg_model.py
│
├── model_utils.py
├── .gitignore
└── README.md

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⚙️ Installation

Clone the repository

git clone https://github.com/Subhaashree06/heart-disease-prediction.git

Move into the project directory

cd heart-disease-prediction

Create virtual environment

python -m venv venv

Activate environment

Windows

venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

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▶️ Running the Application

Start the Flask server:

python app.py

Open the browser and go to:

http://127.0.0.1:5000

Upload the input data to receive the prediction result.

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📊 Prediction Output

The model classifies patients into the following risk categories:

Label	Risk Level
0	Low Risk
1	Medium Risk
2	High Risk
3	Critical Risk

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📈 Future Improvements

• Improve model accuracy with larger datasets
• Add real-time ECG signal analysis
• Deploy the application on cloud platforms
• Add user authentication for healthcare professionals

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👩‍💻 Author

Subhaashree Jagannathan

GitHub: https://github.com/Subhaashree06

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⭐ If you like this project

Give it a star ⭐ on GitHub!