Abstract
Ishaemic heart disease is one of the world's most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy and finally coronary angiography. The diagnostic process is stepwise and the results are interpreted hierarchically, i.e. the next step is necessary only if the results of the former are inconclusive. Because the suggestibility is possible, the results of each step are interpreted individually and only the results of the highest step are valid. On the other hand, Machine Learning methods may be able of objective interpretation of all available results for the same patient and in this way increase the diagnostic accuracy of each step. We conducted many experiments with four learning algorithms and different variations of our dataset (327 patients with completed diagnostic procedures). Our results show that improvements using Machine Learning techniques are reasonable and might find good use in practice.
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References
C. M. Gerson. Test accuracy, test selection, and test result interpretation in chronic coronary artery disease. In C. M. Gerson, editor, Cardiac Nuclear Medicine, pages 309–347. Mc Graw Hill, New York, 1987.
I. Kononenko, E. Šimec, and M. Robnik-Šikonja. Overcoming the myopia of inductive learning algorithms with ReliefF. Applied Intelligence, In press, 1996.
I. Kononenko. Inductive and Bayesian learning in medical diagnosis. Applied Artificial Intelligence, 7:317–337, 1993.
D.E. Rumelhart and J. L. McClelland. Parallel Distributed Processing, volume 1: Foundations. MIT Press, Cambridge, 1986.
S. Weigand, A. Huberman, and D. E. Rumelhart. Predicting the future: a connectionist approach. International Journal of Neural Systems, 1(3), 1990.
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© 1997 Springer-Verlag Berlin Heidelberg
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Kukar, M., Grošelj, C., Kononenko, I., Fettich, J. (1997). An application of Machine Learning in the diagnosis of ischaemic heart disease. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029480
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DOI: https://doi.org/10.1007/BFb0029480
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