Prediction of Heart Disease and Survivability using Support Vector Machine and Naive Bayes Algorithm

Abstract

PurposeIn the present work, we examined the outcomes and accuracy of the Support vector machine (SVM) and the Naive Bayes algorithms on a dataset, to predict whether the patient has heart disease or not, and the patients survival prediction status. MethodThe machine learning procedures were developed using the clinically validated datasets with sixteen attributes from the University of California, Irvines Centre for Machine Learning, and Intelligent Systems. Confusion matrix was used to visualise the accuracy, recall, precision, and error of the models. Statistical analysis was done to prove the model accuracy using the receiver operating characteristic (ROC) curve and area under the curve (AUC). ResultsThe proposed method of heart disease prediction using Naive Bayes had 87 % accuracy. The accuracy for heart survivability models using SVM and Naive Bayes were 88 % and 93 %. The model efficiency for heart survivability using ROC curve with AUC 0.93 for Naive Bayes and AUC 0.91 for SVM. ConclusionSuch prediction systems can help the medical sector to save energy, cost, and time by providing more efficient techniques to forecast decisions with high accuracy. This study will enable the statisticians and researchers to select more efficient and accurate machine learning algorithms to achieve better prediction of the "cardiovascular disease".