This repository contains a Streamlit web application for predicting the likelihood of heart disease based on patient data. The machine learning model used in this app is trained on the Heart Disease UCI dataset and predicts the probability of heart disease based on various input features.
To run this application locally, follow these steps:
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Clone the repository:
git clone https://github.com/your-username/heart-disease-prediction.git cd heart-disease-prediction
Once the Streamlit app is running, open your web browser and go to (http://localhost:8501) (or the URL provided by Streamlit in the terminal). You will see a form where you can input patient details such as age, sex, resting blood pressure, cholesterol levels, etc. After filling in the required information, click the Predict button to see the model's prediction for heart disease.
Input Fields The app includes the following input fields for predicting heart disease:
Age Sex Chest pain type Resting blood pressure Cholesterol levels Fasting blood sugar Resting electrocardiographic results Maximum heart rate achieved Exercise-induced angina ST depression induced by exercise Slope of the peak exercise ST segment Number of major vessels colored by fluoroscopy Thalassemia type Prediction The app predicts the likelihood of heart disease based on the input data using a pre-trained machine learning model.
Contributions to this project are welcome! Here are some ways you can contribute:
Submit bug reports or feature requests by opening an issue. Fork the repository and submit a pull request for enhancements. Improve the documentation by suggesting edits to the README file. Your contributions will help improve the functionality and usability of the heart disease prediction app. Thank you for your interest and support!