The early diagnosis of heart attacks: a neurocomputational approach

A multilayered perceptron (MLP) was trained to diagnose the presence of acute myocardial infarction (heart attack) in patients admitted to an emergency unit with acute chest pain. Two learning algorithms, based on mean-square-error and the log-likelihood function, are compared. Their performance does not differ significantly, but the latter rule converges much more rapidly. Performance in excess of that of the admitting clinicians was achieved for a number of performance indicators, and a protocol for combining the network's diagnosis with that of the clinician is proposed. This results in further improvements in performance, indicating that the MLP can act as a useful decision aid in an emergency context.<
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