Heart Attack Prediction Model

Overview

This project implements a Heart Attack Prediction Machine Learning Model that estimates the likelihood of a heart attack for a patient based on their health parameters. The model uses the Random Forest algorithm and achieves very high accuracy, making it a reliable tool for medical analysis and decision-making.

Applications

• Healthcare Providers: Aid in early diagnosis and risk assessment for heart disease.
• Hospitals: Prioritize patients based on their likelihood of a heart attack, optimizing emergency care.
• Insurance Companies: Assist in risk profiling for health insurance policies.
• Preventive Health Programs: Identify high-risk individuals for targeted interventions.

Heart Attack Risk Prediction Flask App

This project is a Flask-based web application that predicts the risk of a heart attack based on user input. The prediction is powered by a machine learning model.

Features

• Interactive form to input patient details.
• Machine learning-based prediction for heart attack risk.
• Easy-to-use interface with a clean design.

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Installation

1. Clone the repository:

   git clone https://github.com/Onome-Joseph/Heart-Attack-Prediction.git

2. Create a virtual environment (recommended):

   python -m venv venv

   Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

3. Install required dependencies:

    python
    !pip install Flask
    !pip install numpy
    !pip install scikit-learn
    !pip install pandas

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Running the Application

1. Ensure the classifier.pkl file is in the root directory. This file contains the trained machine learning model. If it's missing, the app will not work.

2. Start the Flask server:

   python Heart_attack_FLASK.py

3. Access the web application: Open your browser and go to:

   http://127.0.0.1:5000/
   

4. Fill in the form and get the prediction!

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Project Structure

your-repository-name/
│
├── Heart_attack_FLASK.py        # Flask application script
├── classifier.pkl               # Pre-trained machine learning model
├── requirements.txt             # List of Python dependencies
├── templates/
│   └── front.html               # HTML template for the web app (plain design)
│   └──front2.html               # HTML template for the web app (better design)
├── App preview                  # Layout of the frontend design (front2)
└── README.md                    # Project documentation

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Contributions

Contributions are welcome! Feel free to fork the repository, suggest improvements.