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🫀💻 Heart Disease Prediction Model

This project implements a Heart Disease Prediction Model using Logistic Regression and provides a simple web interface for users to input medical data and get predictions on the likelihood of heart disease. The model is trained using a dataset containing various patient features like age, cholesterol levels, blood pressure, and more, to predict the presence or absence of heart disease.

🔑 Key Features

  • Machine Learning Model: Logistic Regression model trained on a heart disease dataset.
  • Web Application: Simple Flask-based web app for user interaction and predictions.
  • Data Input: Users can input medical data (age, chest pain type, BP, etc.) to receive predictions.
  • Model Persistence: The trained model is saved and loaded using pickle for easy reuse.

📂 Project Structure

  • ML.py: This script handles data preprocessing, model training, and model saving.
  • app.py: This Flask web app script serves the HTML pages, accepts user input, and returns predictions using the trained model.
  • templates/: Contains the HTML files (home.html, after.html) used in the web app.
  • Heart_Disease_Prediction.csv: The dataset used to train the model, containing patient data.

🧠 Model Details

  • Algorithm: Logistic Regression
  • Dataset: The heart disease dataset consists of features such as age, blood pressure, cholesterol levels, etc., to predict whether a patient has heart disease.

Example of data:

Age Sex Chest pain type BP Cholesterol FBS over 120 EKG results Max HR Exercise angina ST depression Slope of ST Number of vessels fluro Thallium Heart Disease
70 1 4 130 322 0 2 109 0 2.4 2 3 3 Presence
64 1 4 128 263 0 0 105 1 0.2 2 1 7 Absence

🚀 How to Run

  1. Clone the repository: 
    git clone https://github.com/yourusername/heart-disease-prediction.git
cd heart-disease-prediction

Prepare the dataset: Ensure that the CSV file (Heart_Disease_Prediction.csv) is in the correct directory as specified in the code.

  1. Train the model: Run the ML.py script to train the Logistic Regression model and generate the ml.pkl file: 
    python ML.py
  2. Run the Flask web application: After the model is saved, start the Flask app: 
    python aap.py
  3. Access the web application: Open your browser and navigate to: 
    http://127.0.0.1:5000/
  4. Make predictions: Enter the required data in the web form, and the application will predict whether the patient has heart disease.

🛠️ Tech Stack

  • Frontend: HTML (Rendered using Flask's Jinja templates)
  • Backend: Flask
  • Machine Learning: Logistic Regression using scikit-learn
  • Data Handling: pandas, numpy
  • Model Persistence: pickle
  • Dataset: CSV file for heart disease prediction

🧩 Future Enhancements

  • Improve the UI/UX of the web application.
  • Integrate more machine learning models (e.g., Random Forest, SVM) to improve prediction accuracy.
  • Deploy the web app to a cloud platform like Heroku or AWS.
  • Allow for real-time predictions via API integrations.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.