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Proceedings Paper

Minimum-risk decisions in the management of suspected heart attack: an application of the Boltzmann perceptron network
Author(s): Robert F. Harrison; R. Lee Kennedy; Stephen J. Marshall
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Paper Abstract

The use of artificial neural networks and Bayesian decision theory is proposed to provide a diagnostic tool which is capable not simply of making a correct decision but of allowing expert knowledge to be incorporated leading to the least risky decision. A neural network is used to estimate a posteriori (class) probabilities conditioned on the input data and the expert knowledge is introduced in the form of subjectively assigned weightings on erroneous decisions. An example of a decision aid for the early diagnosis of heart attacks is presented.

Paper Details

Date Published: 16 September 1992
PDF: 12 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139985
Show Author Affiliations
Robert F. Harrison, Univ. of Sheffield (United Kingdom)
R. Lee Kennedy, Univ. of Sheffield (United Kingdom)
Stephen J. Marshall, Univ. of Sheffield (United Kingdom)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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