Investigation of Alternative Evolutionary Prototype Generation in Medical Classification

Abstract

The response of a computational system to support medical diagnosis should simultaneously be accurate, comprehensible, flexible and prompt in order to be qualified as a reliable second opinion. Based on the above characteristics, the current paper examines the behaviour of two evolutionary algorithms that discover prototypes for each possible diagnosis outcome. The discovered centroids provide understandable thresholds of differentiation among the decision classes. The goal of this paper is to inspect alternative architectures for prototype representation to reach the centroids with desired accuracy and in acceptable time.