Heart disease is one of the leading causes of death worldwide. Early detection of heart disease can significantly improve patient outcomes by enabling timely intervention. This project leverages machine learning techniques to predict the likelihood of heart disease based on various medical attributes and patient data.
- Preprocessing of medical data
- Multiple machine learning models for classification
- Evaluation metrics for model performance
- Visualization of results
- Python
- Machine Learning libraries: scikit-learn, TensorFlow, or PyTorch
- Data manipulation: pandas, numpy
- Data visualization: matplotlib, seaborn
- Clone the repository:
git clone https://github.com/yourusername/Heart-Disease-detection-using-ML.git
- Navigate to the project directory:
cd Heart-Disease-detection-using-ML - Install required dependencies:
pip install -r requirements.txt
This project uses a publicly available heart disease dataset from Kaggle or UCI Machine Learning Repository. Ensure you download the dataset and place it in the data folder.
- Data Preprocessing: Run the preprocessing script to clean and normalize the dataset.
python preprocess.py
- Model Training: Train the model using the provided script.
python train_model.py
- Evaluation: Evaluate the trained model on the test set.
python evaluate_model.py
- Visualization: Visualize the results and performance metrics.
python visualize_results.py
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Commit your changes.
- Push to your branch.
- Open a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.
- The dataset providers for making this research possible.
- Open-source libraries and contributors.
For questions or feedback, please contact [NANDA GOPAL.D] at [nandagopalng2004@gmail.com].