Heart_disease_prediction and Heart-Disease-Prediction-System
These two tools are competitors, as both provide standalone systems for heart disease prediction using machine learning, meaning a user would likely choose one implementation over the other based on their specific needs or preferred algorithms.
About Heart_disease_prediction
chayandatta/Heart_disease_prediction
Heart Disease prediction using 5 algorithms
Compares five classification algorithms (Logistic Regression, Random Forest, Naive Bayes, KNN, Decision Tree) on the UCI Heart Disease dataset, with hyperparameter tuning to optimize accuracy across models. Delivered as a Jupyter notebook enabling interactive exploration of model performance and feature importance visualization. Targets ML beginners seeking hands-on experience with supervised learning fundamentals and algorithm comparison workflows.
About Heart-Disease-Prediction-System
Kumar-laxmi/Heart-Disease-Prediction-System
Heart Disease Prediction System using Machine Learning
Combines Naive Bayes, Decision Tree, Gradient Boosting, and logistic regression algorithms trained on 13 clinical parameters (age, blood pressure, cholesterol, etc.) to classify heart disease risk levels. Built as a Django web application with role-based access for patients, doctors, and admins—patients receive predictions plus locality-based doctor recommendations, while administrators manage datasets and doctor profiles through SQLite persistence.
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