Implementation of Support Vector Machine Algorithm for Heart Disease Risk Identification Using Signal Electrocardiogram

: In the medical world, one of the biggest contributors to death in the world is heart disease. Early detection of the risk of heart disease can increase the chances of recovery and reduce mortality. This research applies the Support Vector Machine (SVM) algorithm to identify the risk of heart disease using Electrocardiogram Signals. The ECG data used was taken from a public database that contained a record of information on the electrical activity of the heart of patients with various heart health conditions. The Support Vector Machine algorithm is applied to classify ECG signals into 2 main classes, namely normal conditions and at-risk conditions. Several methods in data processing, including data normalization and feature selection are used to improve the accuracy and success of the model. The results of the evaluation with this method resulted in accuracy, precision, recall and also F1-score showed that the modeling of this algorithm produced a fairly good classification, with an accuracy of more than 90% in the identification of heart disease risk. This study shows the potential use of this algorithm in automatically detecting the risk of heart disease based on ECG signals, which can be a tool in medical diagnosis. The results show that implementing the SVM strategi with the RBF kernel appears to be a very easy execution when compared to the direct part. An important component that affects the adequacy of an SVM strategy is the parameters of the section and the way the information is handled.