Description

ABSTRACT:-

TO implement an effective framework to propose 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. To take an efficient decision. 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.