Heart Disease Prediction System using Data Mining Classification Techniques: Naïve Bayes, KNN, and Decision Tree

Abstract

© 2020, World Academy of Research in Science and Engineering. All rights reserved. Heart-related illnesses are one of the significant causes of death within the world nowadays. Most people do not realize they have heart disease until it is too late. Some parameters can be used to predict it, such as chest pain type, age, sex; fasting blood sugar; maximum heart rate. In this paper, using Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN), a prediction of heart disease classification is presented. The results show that our proposed data mining technique using Naive Bayes can predict as high as 86% accuracy outperforming the previous works. Besides, Naïve Bayes is the best model in this study since it has the best values in terms of precision, accuracy, and specificity compared to other models.