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:

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

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

   pip install -r requirements.txt

3. Run the Streamlit App: Launch the app with the following command:

   streamlit run app.py
   

License

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

Connect with Me

• LinkedIn: Sunny Kumar
• Contact: Email me at sunnykumar6121997@gmail.com