Diagnosing mitral valve prolapse by improving the predictive power of classifiers

Mitral valve prolapse (MVP) has been described as one of the most common cardiac valvular abnormalities in industrialized countries, and can result in sudden death. This study focused on various feature selection mechanisms that might improve the predictive power of a classifier to diagnose MVP. The experiment included selection mechanisms using classical greedy feature selection approaches (forward selection and backward elimination), a genetic algorithm (GA) approach and a cellular automaton (CA) approach. The main aim of this latest approach is to use CA with GA for the data transformation phase of the knowledge discovery process. The CA-GA approach produced better results than the classical greedy approaches. The subsets of features produced by the GA and CA approaches were most appropriate for the decision tree classifier, for diagnosing MVP with the highest overall class accuracy. More importantly, the CA and GA approaches were also capable of generalizing some important knowledge concerning MVP diagnosis.