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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J.E. Adams, D.R. Abendschein and A.S. Jaffe (1993) Biochemical Markers of Myocardial Injury. Is MB Creatine Kinase the Choice for the 1990s?, Circulation, 88, 750–763.PubMedGoogle Scholar
  2. [2]
    J. Adams, R. Trent and J. Rawles (1993) Earliest Electrocardiographic Evidence of Myocardial Infarction: Implications for Thrombolytic Treatment, British Medical Journal, 307, 409–413.PubMedGoogle Scholar
  3. [3]
    G.A. Carpenter and S. Grossberg (1987) A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine, Computer Vision, Graphics and Image Processing, 37, 54–115. Reprinted in [4], 316–382.Google Scholar
  4. [4]
    G.A. Carpenter and S. Grossberg (Eds.) (1991) Pattern Recognition by Self-Organizing Neural Networks. Cambridge, MA: MIT Press.Google Scholar
  5. [5]
    G.A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds and D.B. Rosen (1992) Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps, IEEE Transactions on Neural Networks, 3(5), 698–712.CrossRefGoogle Scholar
  6. [6]
    G.A. Carpenter, S. Grossberg and J.H. Reynolds (1991) ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network, Neural Networks, 4(5), 565–588.CrossRefGoogle Scholar
  7. [7]
    G.A. Carpenter and A.H. Tan (1993) Rule Extraction, Fuzzy ARTMAP, and Medical Databases, Proceedings of the World Congress on Neural Networks, Volume I, 501–506.Google Scholar
  8. [8]
    J. Downs, R.F. Harrison and S.S. Cross (1995) Evaluating a Neural Network Decision Support Tool for the Diagnosis of Breast Cancer, this volume.Google Scholar
  9. [9]
    J. Downs, R.F. Harrison, R.L. Kennedy and K. Woods (In Press) The Use of Fuzzy ARTMAP to Identify Low Risk Coronary Care Patients, to appear in Proceedings of the 1995 International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA-95). Vienna: Springer-Verlag.Google Scholar
  10. [10]
    S. Grossberg (1987) Competitive Learning: From Interactive Activation to Adaptive Resonance, Cognitive Science, 11(1), 23–63.CrossRefGoogle Scholar
  11. [11]
    R.F. Harrison, C.P. Lim and R.L. Kennedy (1994) Autonomously Learning Neural Networks for Clinical Decision Support. In: E.C. Ifeachor and K.G. Rosen (Eds.) Proceedings of the International Conference on Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED-94), Plymouth, UK, 15–22.Google Scholar
  12. [12]
    T. Kasuba (1993) Simplified Fuzzy ARTMAP, AI Expert, 8(11), 18–25.Google Scholar
  13. [13]
    R.L. Kennedy, R.F. Harrison and S.J. Marshall (1993) Do We Need Computer-Based Decision Support for the Diagnosis of Acute Chest Pain?, Journal of the Royal Society of Medicine, 86, 31–34.PubMedGoogle Scholar
  14. [14]
    S. Marriott and R.F. Harrison (In Press) A Modified Fuzzy ARTMAP Architecture for the Approximation of Noisy Mappings, to appear in Neural Networks.Google Scholar
  15. [15]
    M.E. Stark and J.L. Vacek (1987) The Initial Electrocardiogram During Admission for Myocardial Infarction. Use as a Predictor of Clinical Course and Facility Utilization, Archives of Internal Medicine, 147, 843–846.PubMedGoogle Scholar

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

Personalised recommendations