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Author(s): Cross, S.S. | Downs, J. | Harrison, R.F. | Kennedy, R.L. |
Year: 1996
Citation: Artificial Intelligence in Medicine, 1996, No. 8, pp. 403-428
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 ARTMAP 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 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.
Topics:
Machine Learning,
Applications:
Medical Diagnosis,
Models:
Fuzzy ARTMAP,
