Minimum-risk decisions in the management of suspected heart attack: an application of the Boltzmann perceptron network

Robert F. Harrison; R. Lee Kennedy; Stephen J. Marshall

doi:10.1117/12.139985

16 September 1992 Minimum-risk decisions in the management of suspected heart attack: an application of the Boltzmann perceptron network

Robert F. Harrison, R. Lee Kennedy, Stephen J. Marshall

Author Affiliations +

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139985
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

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Robert F. Harrison, R. Lee Kennedy, and Stephen J. Marshall "Minimum-risk decisions in the management of suspected heart attack: an application of the Boltzmann perceptron network", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139985

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