https://github.com/sunnybibyan/heart_disease_prediction

This project focuses on predicting heart disease using classification models like Random Forest, Gradient Boosting, and XGBoost. A Streamlit web app is built to allow users to input clinical data and receive predictions about their likelihood of heart disease.
https://github.com/sunnybibyan/heart_disease_prediction

heart-disease random-forest-classifier streamlit xgboost

Last synced: 3 months ago
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This project focuses on predicting heart disease using classification models like Random Forest, Gradient Boosting, and XGBoost. A Streamlit web app is built to allow users to input clinical data and receive predictions about their likelihood of heart disease.

• Host: GitHub
• URL: https://github.com/sunnybibyan/heart_disease_prediction
• Owner: SunnyBibyan
• License: mit
• Created: 2024-09-24T19:56:39.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-06-04T15:27:02.000Z (6 months ago)
• Last Synced: 2025-07-30T01:12:19.443Z (4 months ago)
• Topics: heart-disease, random-forest-classifier, streamlit, xgboost
• Language: Jupyter Notebook
• Homepage: https://heartdiseaseprediction-sunnybibyan.streamlit.app/
• Size: 244 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Heart Disease Prediction Using Ensemble Methods 🫀

## Overview

This project applies **ensemble methods** such as **Random Forest** (Bagging) and **Gradient Boosting** (Boosting) to predict the likelihood of heart disease based on clinical features. The project is designed to offer an easy-to-use web interface using **Streamlit**, allowing users to input clinical data and receive predictions.

## Key Features

- **Data Sources**: The dataset includes clinical information like age, sex, cholesterol levels, and more to predict heart disease.

- **Ensemble Models**: Utilizes **Random Forest**, **Gradient Boosting**, and **XGBoost** for prediction.

- **Evaluation Metrics**: Includes precision, recall, F1-score, and ROC-AUC for model evaluation.

- **Streamlit Application**: A web-based interface where users can input their details and get real-time predictions.

## Tools & Technologies

- **Python**: For model building and data processing.

- **Scikit-learn**: Used for machine learning algorithms and metrics.

- **XGBoost**: For optimized gradient boosting.

- **Streamlit**: For building the web app interface.

- **Pandas**: For data manipulation.

- **Matplotlib**, **Seaborn**: For data visualization.

- **Dataset**: Clinical dataset containing features like age, cholesterol, and more.

## Dataset Information

The dataset contains key features that help in predicting heart disease. Some of the most important columns include:

- **Age**: The age of the patient.

- **Sex**: Gender of the patient.

- **Chest Pain Type**: The type of chest pain experienced (categorical).

- **Cholesterol**: Serum cholesterol in mg/dl.

- **Resting Blood Pressure**: Blood pressure at rest.

- **Max Heart Rate**: Maximum heart rate achieved during exercise.

- **Target**: Whether the patient has heart disease (binary: 0 or 1).

## Models

The following ensemble models were used for predicting heart disease:

- **Random Forest**: A bagging technique that builds multiple decision trees and averages their predictions.

- **Gradient Boosting**: A sequential boosting algorithm that focuses on correcting errors made by previous models.

- **XGBoost**: An optimized implementation of Gradient Boosting that improves both speed and performance.

### Model Evaluation Metrics:

- **Accuracy**

- **Precision**

- **Recall**

- **F1-Score**

- **ROC-AUC Score**

## How to Use the Project

1. **Clone the Repository**:

   ```bash

   git clone https://github.com/SunnyBibyan/Heart_Disease_Prediction.git

   cd Heart_Disease_Prediction

   

2. **Install Dependencies**: Install all required libraries by running:

   ```bash

   pip install -r requirements.txt

5. **Run the Streamlit App**: Launch the app with the following command:

   ```bash

   streamlit run app.py

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Connect with Me

- **Contact**: Email me at sunnykumar6121997@gmail.com