Comparison of K-Nearest Neighbor Classification and Naive Bayes Classifier in Analysis of Heart Disease

Heart disease is one of the highest causes of death in several countries, one of which is in Indonesia. There are lots of machine learning algorithms that can be used to make predictions. In this study, we conducted an experiment using the uci repository heart as the dataset used and used two algorithms, namely K-Nearest Neighbors and Naive Bayes. This study aims to find out which algorithm has a better accuracy value in conducting a classification on uci repository heart data, generating confusion matrix values, analyzing and accuracy values in predicting heart disease based on 14 attributes. The results of testing using the confusion matrix is that the KNN algorithm has an accuracy value of 91.25% while Naive Bayes is 88.7%.