A Decision Support System for the Diagnosis of Coronary Artery Disease

Abstract

A rule-based decision support system is presented for the diagnosis of coronary artery disease. The generation of the decision support system is realized automatically using a three stage methodology: (a) induction of a decision tree from a training set and extraction of a set of rules; (b) transformation of the set of rules into a fuzzy model and (c) optimization of the parameters of the fuzzy model. The system is evaluated using 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Ten fold cross validation was employed and the average sensitivity and specificity obtained was 80% and 65% respectively. Our approach provides diagnosis based on easily acquired features and, since it is rule based, is able to provide interpretation for the decisions made