omidcodes/ML-heart-disease-prediction

Machine learning project using the Kaggle Heart Disease Health Indicators dataset to predict heart disease risk. Covers preprocessing, EDA, and training models (Logistic Regression, Random Forest, XGBoost, SVM, KNN, Decision Tree, Naive Bayes) with evaluation via Accuracy, ROC-AUC, and Confusion Matrix.

21
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Experimental

This tool helps healthcare professionals and researchers analyze health survey data to predict an individual's risk of heart disease. By inputting patient health indicators, it generates a prediction of heart disease likelihood and evaluates various predictive models, helping to identify key risk factors. It's designed for medical analysts, public health researchers, or data scientists working in healthcare.

No commits in the last 6 months.

Use this if you need to build and compare machine learning models to assess heart disease risk from existing patient health data.

Not ideal if you need a real-time diagnostic tool for immediate patient care rather than a research or analytical prediction system.

predictive-health cardiology-research public-health-analytics medical-risk-assessment
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 4 / 25
Maturity 15 / 25
Community 0 / 25

How are scores calculated?

Stars

8

Forks

Language

Jupyter Notebook

License

MIT

Last pushed

Aug 20, 2025

Commits (30d)

0

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