the entropy triangle method (etm): a novel framework for the prevention of cardiac arrhythmia with a review of more than 10,000 patients

One of the most important problems in medicine is to facilitate prediction. in this study, we propose entropy triangle method, a novel framework for predicting heart rhythms using a novel machine learning technique. this framework includes three steps: feature engineering, entropy triangle oversampling, and disease prediction. the dataset used in this study is a 12-lead electrocardiogram (ecg) arrhythmia research database with 10,646 patients. this dataset contains 11 different heart rhythms (5 sinus rhythms and 6 non-sinus rhythms). in this article, we introduce two firsts in machine learning and medicine that can predict non-sinus rhythm with over 85% accuracy. our experimental results show, among others, that the most accurate classifier based on entropy triangles and the most useful oversampling are the supported vector classifiers and oversampling techniques for shark scent.