Abstract
In the past decades Machine Learning tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not used in practice. One reason for this is that it is dificult to obtain an unbiased estimation of diagnose’s reliability. We propose a general framework for reliability estimation, based on transductive inference. We show that our reliability estimation is closely connected with a general notion of significance tests. We compare our approach with classical stepwise diagnostic process where reliability of diagnose is presented as its post-test probability. The presented approach is evaluated in practice in the problem of clinical diagnosis of coronary artery disease, where significant improvements over existing techniques are achieved.
This paper represents a part of the author’s doctoral dissertation.
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References
G. A. Diamond and J. S. Forester. Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. New England Journal of Medicine, 300:1350, 1979.
J. Dougherty, R. Kohavi, and M. Sahami. Supervised and unsupervised discretization of continuous features. In Proc. ICML’95, pages 194–202. Morgan Kaufmann, 1995.
A. Gammerman, V. Vovk, and V. Vapnik. Learning by transduction. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 148–155, Madison, Wisconsin, 1998.
A. L. Gibbs and F. E. Su. On choosing and bounding probability metrics. Technical report, Cornell School of Operations Research, 2000.
M. Kukar. Estimating classifications’ reliability. PhD thesis, University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia, 2001. In Slovene.
M. Kukar and C. Grošelj. Machine learning in stepwise diagnostic process. In Proc. Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, pages 315–325, Aalborg, Denmark, 1999.
M. Kukar, C. Grošelj, I. Kononenko, and J. Fettich. An application of machine learning in the diagnosis of ischaemic heart disease. In Proc. Sixth European Conference of AI in Medicine Europe AIME’97, pages 461–464, Grenoble, France, 1997.
M. Kukar, I. Kononenko, C. Grošelj, K. Kralj, and J. Fettich. Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine, 16(1):25–50, 1999.
M. Li and P. Vitányi. An introduction to Kolmogorov complexity and its applications. Springer-Verlag, New York, 2nd edition, 1997.
M. Olona-Cabases. The probability of a correct diagnosis. In J. Candell-Riera and D. Ortega-Alcalde, editors, Nuclear Cardiology in Everyday Practice, pages 348–357. Kluwer, 1994.
B. H. Pollock. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovasc. Rev. Rep. 4, pages 367–375, 1983.
C. Saunders, A. Gammerman, and V. Vovk. Transduction with confidence and credibility. In Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999.
[13] V. Vovk, A. Gammerman, and C. Saunders. Machine learning application of algorithmic randomness. In Proceedings of the 16th International Conference on Machine Learning (ICML’99), Bled, Slovenija, 1999.
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Kukar, M. (2001). Making Reliable Diagnoses with Machine Learning: A Case Study. In: Quaglini, S., Barahona, P., Andreassen, S. (eds) Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science(), vol 2101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48229-6_12
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DOI: https://doi.org/10.1007/3-540-48229-6_12
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