Abstract:Purpose:
This paper compares five supervised learning algorithms (support vector machines, k-nearest neighbor, decision tree, random forest, and AdaBoost) for predicting heart disease and examines the impact of normalization and GridSearch hyper-parameter tuning on model performance.
Methods:
The study utilizes the Cleveland database from the University of California-Irvine (UCI) repository, comprising data on 918 instances of heart disease patients with 12 attributes. Eleven attributes serve as predictors, … Show more
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