A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)

Abstract

The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of support vector machine, decision trees, and logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease.