Heart Disease Prediction using Random Forest Classifier

Overview

Heart disease is a leading cause of mortality worldwide, and early prediction is crucial for effective preventive measures. This project presents a Heart Disease Prediction Model using Random Forest Classification, leveraging machine learning techniques to accurately predict the presence of heart disease in patients.

This is the dataset which was used in this project. Here is the code.

Table of Contents

1. Features
2. Installation
3. Methodology
4. Results
5. Contributing

Features

• Predicts the presence of heart disease based on various clinical parameters.
• Utilizes Random Forest Classification for robust predictions.
• Provides insights into the importance of different features in predicting heart disease.
• Achieves high accuracy and outperforms traditional machine learning algorithms.

Installation

1. Clone the repository:

git clone https://github.com/your-username/heart-disease-prediction.git
cd heart-disease-prediction

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables (if applicable):

cp .env.example .env

4. Run the application:

python main.py

Methodology

The methodology section outlines the research design, data collection methods, and the implementation of the Random Forest Classification algorithm. The key steps include:

1. Data Preprocessing: Handling missing values, feature selection, and data splitting.
2. Model Training: Implementing the Random Forest Classification model.
3. Model Evaluation: Using performance metrics such as accuracy, precision, recall, and F1-score to evaluate the model.

Results

Our experimental results show that the proposed Heart Disease Prediction Model using Random Forest Classification achieves high accuracy in predicting the presence of heart disease, outperforming other traditional machine learning algorithms. The model's performance metrics are as follows:

• Accuracy: Over 90%
• Precision: High
• Recall: High
• F1-Score: High

Contributing

Contributions are welcome! Follow these steps to contribute:

1. Fork the repository.
2. Create a new branch: 'git checkout -b feature-branch-name'
3. Make your changes.
4. Commit your changes: 'git commit -m 'Add some feature'
5. Push to the branch: 'git push origin feature-branch-name'
6. Open a pull request.

Feel free to contact us for any questions or suggestions!