Application of the Fuzzy ARTMAP Neural Network Model to Medical Pattern Classification Tasks

This paper presents research into the application of the fuzzy ARTMAP neural network model to medical pattern classification tasks. A number of domains, both diagnostic and prognostic, are considered. Each such domain highlights a particularly useful aspect of the model. The first, coronary care patient prognosis, demonstrates the ATMAP voting strategy involving "pooled" decision-making using a number of networks, each of which has learned a slightly different mapping of input features to pattern classes. The second domain, breast cancer diagnosis, demonstrates the model's symbolic rule extraction capabilities which support the validation and explanation of a network's predictions. The final domain, diagnosis of acute myocardial infarction, demonstrates a novel category pruning technique allowing the performance of a trained network to be altered so far as to favour predictions of one class over another (e.g. trading sensitivity for specificity or vice versa). It also introduces a "cascaded" variant of the voting strategy intended to allow identification of a subset of cases which the network has a very high certainty of classifying correctly.