Comparison of Accuracy Level of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) Algorithms in Predicting Heart Disease

Heart disease receives a lot of attention in medical research because of its great effect on the human health state. Based on cases in 2008, it was estimated that more than 3 million deaths were caused due to heart disease. This study aims to compare two algorithms and find which algorithm can be utilized appropriately in predicting the accuracy of heart disease data and has the benefit to consider the health problems regarding the percentage of heart disease as well as being an accurate information material. This study used a comparison of the Support Vector Machine (SVM) algorithm and the K-Nearest Neighbor (KNN) algorithm. The method used is the Classification method, one of the techniques in Machine Learning, to find parameters in a linear equation that can map inputs and outputs. The results show that the SVM algorithm testing with normalization had better accuracy results compared to KNN algorithm either with or without normalization continued to produce poor accuracy. The SVM classification results without normalization were 84.61% and with normalization was 90.10%, while the KNN algorithm showed an accuracy of 64.83% and 81.31% with normalization.