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.
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.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Aug 20, 2025
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