The Entropy Triangle Method (ETM): A novelframework for the prevention of cardiacarrhythmia with a review of more than ۱۰,۰۰۰patients

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 threesteps: feature engineering, entropy triangle oversampling, and disease prediction. The dataset used in this study is a ۱۲-leadelectrocardiogram (ECG) arrhythmia research database with ۱۰,۶۴۶ patients. This dataset contains ۱۱ different heart rhythms(۵ sinus rhythms and ۶ non-sinus rhythms). In this article, we introduce two firsts in machine learning and medicine that canpredict non-sinus rhythm with over ۸۵% accuracy. Our experimental results show, among others, that the most accurateclassifier based on entropy triangles and the most useful oversampling are the supported vector classifiers and oversamplingtechniques for shark scent

کلیدواژه ها:

Classification ، prevention ، Entropy Triangle ، Heart Rhythm ، ECG.