Heart-Disease-Prediction and Heart-Disease-Prediction-System-using-Machine-Learning
These are direct competitors offering functionally equivalent heart disease prediction systems built with machine learning, where the kb22 implementation demonstrates superior performance (87% accuracy) and significantly greater community adoption (266 vs 5 stars).
About Heart-Disease-Prediction
kb22/Heart-Disease-Prediction
The project involves training a machine learning model (K Neighbors Classifier) to predict whether someone is suffering from a heart disease with 87% accuracy.
Implements a complete ML pipeline using scikit-learn's KNeighborsClassifier with hyperparameter tuning via GridSearchCV to optimize the k-value selection. The workflow encompasses data preprocessing, feature scaling with StandardScaler, train-test splitting, and model evaluation using confusion matrices and classification metrics. Targets the pandas/scikit-learn ecosystem for rapid prototyping of medical classification tasks.
About Heart-Disease-Prediction-System-using-Machine-Learning
nano-bot01/Heart-Disease-Prediction-System-using-Machine-Learning
A Heart Disease Prediction System built on machine learning
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