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A prototype neural network decision-support tool for the early diagnosis of acute myocardial infarction

  • Joseph Downs
  • Robert F Harrison
  • R Lee Kennedy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 934)

Abstract

An application of the ARTMAP neural network model to the early diagnosis of acute myocardial infarction is described. Performance results are given for 10 individual ARTMAP networks, and for combinations of the networks using “pooled” decision making (the so-called voting strategy). Category nodes are pruned from the trained networks in different ways so as to improve accuracy, sensitivity and specificity respectively. The differently pruned networks are employed in a novel “cascaded” variation of the voting strategy. This allows a partitioning of the test data into predictions with a high and a lower certainty of being correct, providing the diagnosing clinician with an indication of the reliability of an individual prediction.

Keywords

Acute Myocardial Infarction Vote Strategy Category Class Category Cluster Category Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Joseph Downs
    • 1
  • Robert F Harrison
    • 1
  • R Lee Kennedy
    • 2
  1. 1.Dept. of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
  2. 2.Dept. of MedicineUniversity of Edinburgh Western General HospitalEdinburghUK

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