A Compassion of Three Data Miming Algorithms for Heart Disease Prediction

Abstract

Heart disease is one of the most common causes of death worldwide. Real-time methods for forecasting heart disease from medical data sources that explain a patient's current health status are discussed in this paper. The proposed system's main aim is to find the best data mining algorithm for predicting heart disease with high accuracy. We suggested using Decision Tree (DT), Support Vector Machine (SVM) and Naïve Bayes (NB) algorithms. All of these algorithms are classified as supervised learning and work better with training data. The main purpose of using three algorithms is to see which one is the best at predicting heart disease. The result shows that the DT algorithm provides the best accuracy with less training time when compared to SVM and Naïve Bayes(NB).