Supporting Diagnostics of Coronary Artery Disease with Neural Networks

  • Matjaž Kukar
  • Ciril Grošelj
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)


Coronary artery disease is one of its most important causes of early mortality in western world. Therefore, clinicians seek to improve diagnostic procedures in order to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics is often performed in a sequential manner, where the four diagnostic steps typically consist of evaluation of (1) signs and symptoms of the disease and electrocardiogram (ECG) at rest, (2) sequential ECG testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography, that is considered as the “gold standard” reference method. Our study focuses on improving diagnostic and probabilistic interpretation of scintigraphic images obtained from the penultimate step. We use automatic image parameterization on multiple resolutions, based on spatial association rules. Extracted image parameters are combined into more informative composite parameters by means of principle component analysis, and finally used to build automatic classifiers with neural networks and naive Bayes learning methods. Experiments show that our approach significantly increases diagnostic accuracy, specificity and sensitivity with respect to clinical results.


multi-layered perceptron radial basis function network coronary artery disease medical diagnostics explanation 


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  1. 1.
    Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321–355 (1988)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing. Foundations, vol. 1. MIT Press, Cambridge (1986)Google Scholar
  3. 3.
    Diamond, G.A., Forester, J.S.: Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. New England Journal of Medicine 300(1350) (1979)Google Scholar
  4. 4.
    General Electric. Ectoolbox protocol operator’s guide (2001)Google Scholar
  5. 5.
    Gamberger, D., Lavrac, N., Krstacic, G.: Active subgroup mining: a case study in coronary heart disease risk group detection. Artif. Intell. Med. 28(1), 27–57 (2003)CrossRefGoogle Scholar
  6. 6.
    Garcia, E.V., Cooke, C.D., Folks, R.D., Santana, C.A., Krawczynska, E.G., De Braal, L., Ezquerra, N.F.: Diagnostic performance of an expert system for the interpretation of myocardial perfusion spect studies. J. Nucl. Med. 42(8), 1185–1191 (2001)Google Scholar
  7. 7.
    Kukar, M., Kononenko, I.: Cost-sensitive learning with neural networks. In: Proc. European Conference on Artificial Intelligence ECAI 1998, Brighton, UK, pp. 445–449 (1998)Google Scholar
  8. 8.
    Kukar, M., Kononenko, I., Grošelj, C., Kralj, K., Fettich, J.: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine 16(1), 25–50 (1999)CrossRefGoogle Scholar
  9. 9.
    Kukar, M., Šajn, L., Grošelj, C., Grošelj, J.: Multi-resolution image parametrization in stepwise diagnostics of coronary artery disease. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 119–129. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Kurgan, L.A., Cios, K.J., Tadeusiewicz, R.: Knowledge discovery approach to automated cardiac spect diagnosis. Artif. Intell. Med. 23(2), 149–169 (2001)CrossRefGoogle Scholar
  11. 11.
    Lindahl, D., Palmer, J., Pettersson, J., White, T., Lundin, A., Edenbrandt, L.: Scintigraphic diagnosis of coronary artery disease: myocardial bull’s-eye images contain the important information. Clinical Physiology 6(18) (1998)Google Scholar
  12. 12.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  13. 13.
    Nixon, M., Aguado, A.S.: Feature Extraction and Image Processing, 2nd edn. Academic Press, Elsevier (2008)Google Scholar
  14. 14.
    Ohlsson, M.: WeAidU–a decision support system for myocardial perfusion images using artificial neural networks. Artificial Intelligence in Medicine 30, 49–60 (2004)CrossRefGoogle Scholar
  15. 15.
    Olona-Cabases, M.: The probability of a correct diagnosis. In: Candell-Riera, J., Ortega-Alcalde, D. (eds.) Nuclear Cardiology in Everyday Practice, pp. 348–357. Kluwer, Dordrecht (1994)CrossRefGoogle Scholar
  16. 16.
    Slomka, P.J., Nishina, H., Berman, D.S., Akincioglu, C., Abidov, A., Friedman, J.D., Hayes, S.W., Germano, G.: Automated quantification of myocardial perfusion spect using simplified normal limits. J. Nucl. Cardiol. 12(1), 66–77 (2005)CrossRefGoogle Scholar
  17. 17.
    Šajn, L., Kononenko, I.: Multiresolution image parametrization for improving texture classification. EURASIP J. Adv. Signal Process 2008(1), 1–12 (2008)zbMATHGoogle Scholar
  18. 18.
    Štrumbelj, E., Kononenko, I.: An efficient explanation of individual classifications using game theory. Journal of Machine Learning Research 11, 1–18 (2010)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Matjaž Kukar
    • 1
  • Ciril Grošelj
    • 2
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Nuclear Medicine DepartmentUniversity Medical Centre LjubljanaLjubljanaSlovenia

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