Key result
A rule-based decision support system for diagnosing coronary artery disease achieved an average sensitivity of 80% and specificity of 65% using 10-fold cross-validation.
Why the study?
Does a rule-based decision support system accurately diagnose coronary artery disease based on clinical features?
Does a rule-based decision support system accurately diagnose coronary artery disease based on clinical features?
A rule-based fuzzy decision support system achieved 80% sensitivity and 65% specificity for diagnosing coronary artery disease using easily acquired clinical features.
Performance in cross-validation warrants prospective testing; leaves open clinical utility of rule-based CAD diagnosis.
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
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Tsipouras et al. (2006) studied Coronary artery disease (n=199). Rule-based decision support system was evaluated on Average sensitivity and specificity for diagnosis of coronary artery disease. A rule-based decision support system for diagnosing coronary artery disease achieved an average sensitivity of 80% and specificity of 65% using 10-fold cross-validation.
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