Application Of Support Vector Machine Method To Predict Heart Disease - Simatupang, Golfrid Heraldi; Ompusunggu, Elvis (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2024)
Heart attack disease is when the arteries are blocked by fatty deposits This results in symptoms like chest discomfort and dyspnea. Furthermore, Damage to the heart muscle can result from obstructed or reduced blood flow to the heart. Heart attack disease remains Indonesia’s greatest cause of death as of right now. The current problem is that it is very difficult to predict heart disease and identify heart disease. The right method is needed to predict heart disease. The purpose of this study was to calculate the level of accuracy of the Support Vector Machine method in predicting heart attack disease. The research findings and data analysis conducted utilizing the Support Vector Machine algorithm yielded an accuracy rate of 91.8%. Thus, it can be said that in comparison to the K-Nearest Neighbor approach, the support vector machine algorithm is superior in predicting the development of heart attack disease, which achieved an accuracy of 88%, and Logistic Regression, which achieved 83% accuracy. Keywords: Heart Attack, Support Vector Machine, Prediction.