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.
- 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
picklefor easy reuse.
- 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.
- 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 |
- 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.
- Train the model: Run the ML.py script to train the Logistic Regression model and generate the ml.pkl file:
python ML.py
- Run the Flask web application: After the model is saved, start the Flask app:
python aap.py
- Access the web application: Open your browser and navigate to:
http://127.0.0.1:5000/
- Make predictions: Enter the required data in the web form, and the application will predict whether the patient has heart disease.
- 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
- 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.
This project is licensed under the MIT License - see the LICENSE file for details.