Abstract
Using a new method published by the first author this paper discovers the ranked class profiles of key inputs used by a multi-layer perceptron (MLP) network that classifies low back pain patients into three diagnostic classes. It is shown how the validation of the class profiles leads to the discovery of 4 mis-diagnosed training cases and 2 further cases which were not relevant. By interpreting the test cases mis-classified by the MLP and comparing them with the validated class profiles a number of test cases were also found to have been mis-diagnosed by the clinicians. It is shown how the class profiles were used to develop a more optimal network with approximately half the number of inputs and only a marginally reduced test performance.
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© 2000 Springer-Verlag London
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Vaughn, M.L., Cavill, S.J., Taylor, S.J., Foy, M.A., Fogg, A.J.B. (2000). The Use of a Knowledge Discovery Method for the Development of a Multi-layer Perceptron Network that Classifies Low Back Pain Patients. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_48
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_48
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
Online ISBN: 978-1-4471-0513-8
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