A Comparison of Heart Abnormalities Detection on ECG using KNN and Decision Tree

Abstract

In life, the heart is always required to always be in good condition because the heart serves to pump blood that carries nutrients throughout the body. Impaired heart function can be fatal to human health, even some heart disorders can lead to death. To be able to detect the presence of abnormalities or disorders of the heart, it must be known in advance the working rhythm or signal pattern of the heart itself. In this study the algorithm KNN and the decision tree of the three algorithms was used to search for the best results so that the initial diagnosis error can be minimized. K-NN and the decision tree give the best results with an accuracy of 97.373% and 95.87%, respectively. Early detection of cardiovascular disease can be done with ECG wave analysis based on Artificial Intelligence to make the process more efficient. In this research, machine learning can classify abnormal ECGs with maximum accuracy of 97.373%.