Abstract
Abstract In this study, the performances of 33 supervised and unsupervised machine learning methods for the automatic classification of cardiac arrhythmias as normal or abnormal using the MIT BIH Arrhythmia Database are evaluated. Electrocardiogram signals from the MLII and V1 leads are segmented into fixed-length windows aligned to the R peak, with raw amplitude values used as model inputs without feature extraction. In the supervised analysis, various statistical and ensemble classifiers are evaluated, while the unsupervised analysis assesses Isolation Forest, One Class support vector machines (SVM), Local Outlier Factor, Elliptic Envelope, and an autoencoder model. The results demonstrate that, when labeled data are available, supervised methods, particularly K nearest neighbors (KNN) and Random Forest, provide higher accuracy and more balanced classification compared with unsupervised models. Unsupervised approaches, on the other hand, are positioned as complementary tools for arrhythmia screening and early warning when labeled data are limited.