Heart-Disease-Prediction and Heart-Disease-Prediction-using-machine-and-deep-learning-techniques
These two projects are competitors, as both aim to predict heart disease using machine learning, with the first explicitly stating a higher accuracy and using a K-Neighbors Classifier, while the second uses a broader description of machine and deep learning techniques.
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-using-machine-and-deep-learning-techniques
nano-bot01/Heart-Disease-Prediction-using-machine-and-deep-learning-techniques
Heart Disease Prediction using machine and deep learning techniques works on heart dataset
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