ML Model to predict which patients are most likely to suffer from a heart disease in the near future using the features given.
Heart Disease Prediction - Predicting the likelihood of heart disease using machine learning.
Table of Contents
1.Introduction -> The "Heart Disease Prediction" project aims to provide a machine learning solution to predict the probability of an individual having heart disease based on certain medical attributes. This model could be used by medical professionals to assess the risk of heart disease in patients.
2.Features -> Input: The model takes in attributes such as age, sex, chest pain type, blood pressure, cholesterol levels, etc. Output: Provides a probability score indicating the likelihood of heart disease.
3.Getting Started -> To use the "Heart Disease Prediction" model, follow these steps:
- Installation: Clone this repository and navigate to the project directory.
git clone https://github.com/ReX027/heartDiseaseML_Model.git
cd heartDiseaseML_Model
- Setup: Create a virtual environment and install dependencies.
- Data Preparation: Prepare your input data in CSV format with required attributes.
- Model Training: Train the machine learning model using the prepared dataset.
python train_model.py
4.Usage -> Once the model is trained, you can make predictions using the following steps: - Load Model: from heart_disease_model import HeartDiseaseModel model = HeartDiseaseModel.load_model('model.pkl')
- Prepare input data with attributes like age, sex, chest pain type, etc.
- Use the loaded model to make predictions.
5.Contributing -> Contributions are welcome! To contribute to the "Heart Disease Prediction" project: - Report any issues or bugs by opening an issue. - Fork the repository, make changes, and submit a pull request for review. - Follow the coding and testing standards outlined in the project.
6.Contact -> For questions or suggestions, feel free to contact me at [email protected]