Department of Automatic Control and Systems Engineering
Abstract
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
