Decision Support System for Heart Disease Diagnosing Using K-NN Algorithm

Heart disease is a notoriously dangerous disease whichpossibly causing the death. An electrocardiogram (ECG) is used fora diagnosis of the disease. It is often, however, a fault diagnosis by adoctor misleads to inappropriate treatment, which increases a riskof death. This present work implements k-nearest neighbor (K-NN)on ECG data to get a better interpretation which expected to help adecision making in the diagnosis. For experiment, we use an ECGdata from MIT BIH and zoom in on classification of three classes;normal, myocardial infarction and others. We use a single decisionthreshold to evaluate the validity of the experiment. The resultshows an accuracy up to 87% with a value of K = 4