Genetic programming for knowledge discovery in chest pain diagnosis

This work aims at discovering classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules we have used genetic programming as well as some concepts of data mining, with emphasis on the discovery of comprehensible knowledge. The fitness function used combines a measure of rule comprehensibility with two usual indicators in medical domain: sensitivity and specificity. Results regarding the predictive accuracy of the discovered rule set as a whole and the predictive accuracy of individual rules are presented and compared to other approaches.