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A fuzzy prototype-based method to predict myocardial infarction

This paper presents a machine learning method that predict the risk of myocardial infarction from angiographic descriptions of coronary stenoses, taking into account the imprecision of the source data. Assuming that the description of coronary stenoses is related to the risk of myocardial infarction, we consider as a prototype a virtual concept which is representative of an event-defined class of coronary stenoses (occurrence of myocardial infarction). Each lesion is described by a set of characteristics that are represented by fuzzy sets. The predictive learning method consists in a k-nearest neighbors clustering with a defined measure of similarity, and a prototype supervised induction function from the most similar stenoses, taking into account their degree of typicality. The algorithm of the computer prediction system is described in its two successive phases (learning and classification), and preliminary results are presented using a coronary stenoses database. The learning phase outputs the prototypes from a large part of the original database. The evaluation phase classifies the remaining stenoses according to their similarity to the prototypes. This paper emphasizes the feasibility of the symbolic learning to predict the risk of myocardial infarction with descriptions of angiographic coronary lesions.