https://github.com/machinelearningprodigy/heart-disease-prediction
Here’s a sHeart Disease Prediction App is a Streamlit-based tool that predicts heart disease risk using an XGBoost model. Enter your health details, get instant predictions, and explore insightful visualizations! 🚀❤️
https://github.com/machinelearningprodigy/heart-disease-prediction
classification pandas plotly streamlit xgboost
Last synced: 3 months ago
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Here’s a sHeart Disease Prediction App is a Streamlit-based tool that predicts heart disease risk using an XGBoost model. Enter your health details, get instant predictions, and explore insightful visualizations! 🚀❤️
- Host: GitHub
- URL: https://github.com/machinelearningprodigy/heart-disease-prediction
- Owner: machinelearningprodigy
- Created: 2024-01-29T14:00:26.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-27T18:38:28.000Z (11 months ago)
- Last Synced: 2025-10-14T21:38:33.697Z (4 months ago)
- Topics: classification, pandas, plotly, streamlit, xgboost
- Language: Python
- Homepage: https://heartdisease-n3ym95ohbayrwqew6g4bxp.streamlit.app/
- Size: 44.9 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ❤️ Heart Disease Prediction App
Welcome to the **Heart Disease Prediction App**, a user-friendly tool built with **Streamlit** that helps predict the likelihood of heart disease based on key health indicators. This app utilizes an **XGBoost** model trained on relevant medical features to provide insights into heart disease risk.
## 🚀 Features
- **Interactive UI:** Easy-to-use sliders, radio buttons, and dropdowns for data input.
- **Machine Learning Model:** Uses a trained **XGBoost** classifier to predict heart disease.
- **Data Visualizations:** Dynamic **Plotly** charts to understand how different factors influence heart disease.
- **Responsive Design:** Works seamlessly on both desktop and mobile devices.
## 📊 How It Works
1. Enter your **health details** such as age, gender, blood pressure, cholesterol levels, chest pain type, etc.
2. Click the **"Predict"** button to get an instant prediction.
3. View the result in a color-coded message box:
- 🟢 **No Heart Disease** (Green background)
- 🔴 **Heart Disease Present** (Red background)
4. Explore insights on different **risk factors** through visualizations.
## 🛠 Installation & Usage
To run this app locally, follow these steps:
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/machinelearningprodigy/Heart-Disease-Prediction.git
cd Heart-Disease-Prediction
```
### 2️⃣ Install Dependencies
Ensure you have Python installed, then run:
```bash
pip install -r requirements.txt
```
### 3️⃣ Run the Streamlit App
```bash
streamlit run app.py
```
## 🏗 Technologies Used
- **Python 3.x**
- **Streamlit** (for the web interface)
- **XGBoost** (for machine learning model)
- **Pandas** (for data processing)
- **Plotly** (for visualizations)
## 📦 Requirements
This project requires the following dependencies:
```txt
pandas==2.0.3
plotly==5.9.0
streamlit==1.30.0
xgboost==2.0.3
```
## 🎯 Future Enhancements
- ✅ Add more **health parameters** to improve prediction accuracy.
- ✅ Implement a **database** to store user history.
- ✅ Enhance **UI design** for a better user experience.
## 🤝 Contributing
Want to improve this project? Feel free to fork, create a new branch, and submit a pull request!
## 📜 License
This project is open-source and available under the **MIT License**.
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📧 **Have any questions?** Feel free to reach out or raise an issue in the repository!