This project focuses on predicting the presence of heart disease in patients based on various medical attributes. The goal is to develop a machine learning model that can assist in early diagnosis and treatment decisions.
The dataset used for this project is sourced from the UCI Machine Learning Repository. It contains information collected by multiple institutions, including the Hungarian Institute of Cardiology and University Hospitals in Zurich and Basel.
The project follows the following structure:
data/: Directory to store the datasetREADME.md: This file, providing an overview of the project.
To run the project, follow these steps:
- Clone the repository:
git clone https://github.com/Rhythm-Divine/heart-disease-prediction.git - Navigate to the project directory:
cd heart-disease-prediction - Install the required libraries
- Download the dataset from Kaggle and place it in the
data/directory. - Open and run the Jupyter notebooks in the
notebooks/directory for data exploration, preprocessing, modeling, and evaluation.
The model's performance is evaluated based on accuracy, aiming to achieve at least 85% accuracy in predicting heart disease. The results, insights, and potential applications are discussed in the Jupyter notebooks and documented in the project report.
Potential future steps for this project include:
- Refining the model with additional feature engineering and hyperparameter tuning.
- Exploring different machine learning algorithms for comparison.
- Deploying the trained model as a web application or API for practical use.
Contributions and suggestions are welcome! Feel free to open an issue or submit a pull request.