This research project aims to improve the accuracy of heart failure prediction models using hyperparameter optimization and feature selection. 8 supervised learning classification algorithms namely Logistic Regression, KNN, Naive Bayes, SVM, Decision Tree, Random Forest, LightGBM and XGBoost predictions were analyzed and improved upon using hyperparameter optimization and feature selection. An accuracy of 87.83% was achieved using Random Forest for all 12 features and KNN for top 5 features (selected after feature selection).
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