CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS

Heart attack which is the main cause of death for both men and women leader among deaths due to heart diseases. Therefore, early diagnosis very important patients who are having a attack. study aimed develop clinical decision support system help physicians. In study, variables were obtained accompanied by physicians statistical analysis methods, where optimum selected from these considering patient’s unconscious state in some cases. Different models developed using probit regression, tree, SVM, ANN methods. As result, compared evaluated. Consequently, best model was with variables. addition these, proposed significantly noticed sensitivity 98% specificity 93.7% similar studies literature. By system, it possible determine whether patient has or not physician process