Abstract
Machine learning techniques can be used to extract knowledge from data stored in medical databases. In our application, various machine learning algorithms were used to extract diagnostic knowledge which may be used to support the diagnosis of sport injuries. The applied methods include variants of the Assistant algorithm for top-down induction of decision trees, and variants of the Bayesian classifier. The available dataset was insufficient for reliable diagnosis of all sport injuries considered by the system. Consequently, expert-defined diagnostic rules were added and used as pre-classifiers or as generators of additional training instances for diagnoses for which only few training examples were available. Experimental results show that the classification accuracy and the explanation capability of the naive Bayesian classifier with the fuzzy discretization of numerical attributes were superior to other methods and estimated as the most appropriate for practical use.
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REFERENCES
Aha, D., Kibler, D., and Albert, M. Instance-based learning algorithms. Mach. Learn.6:37–66, 1991.
Anderson, J. A., and Rosenfeld, E., Neurocomputing: Foundations of Research, The MIT Press, 1988.
Cestnik, B., Estimating probabilities: A crucial task in machine learning, Proc. European Conf. on Artificial Intelligence, Stockholm, August, 1990, pp. 147–149.
Cestnik, B., Kononenko, I., and Bratko, I., ASSISTANT 86: A knowledge elicitation tool for sophisticated users. Progress in Machine Learning(I. Bratko and N. Lavrac, eds.). Sigma Press, Wilmslow, pp. 31–45, 1987.
Clark, P., and Boswell, R. Rule induction with CN2: Some recent improvements. In Proc. Fifth European Working Session on Learning, Springer, Berlin, pp. 151–163, 1991.
Clark, P., and Niblett, T., The CN2 induction algorithm. Mach. Learn.3(4):261–283.
Dasarthy, B. V. (ed.). Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, IEEE Computer Society Press, Los Alamitos, CA, 1990.
Hunt, E., Martin, J., and Stone, P., Experiments in Induction, Academic Press, New York.
Kira, K., and Rendell, L., A practical approach to features selection. Proc. Int. Conf. on Machine Learning ICML-92(Aberdeen, July 1992). (D. Sleeman and P. Edwards, eds.), Morgan Kaufmann, pp. 249–256.
Kononenko, I., Inductive and Bayesian learning in medical diagnosis. Appl. Artificial Intell.7:317–337.
Kononenko, I., Estimating attributes: Analysis and extensions of RELIEF. Proc. European Conf. on Machine Learning ECML-94(Catania, Sicily, April 1994) (F. Bergadano and L. de Readt, eds.), Springer Verlag, Berlin, pp. 171–182.
Kononenko, I., and Bratko, I., Information-based evaluation criterion for classifier's performance. Mach. Learn.6(1):67–80, 1991.
Kononenko, I., and Kukar, M., Machine learning for medical diagnosis. Proc. Workshop on Computer-Aided Data Analysis in Medicine, CADAM-95(Bled, November 1995) (N. Lavrac, ed.), IJS Scientific Publishing, Ljubljana.
Kononenko, I., and %imec, E., Induction of decision trees using RELIEFF. Proc. of ISSEK Workshop on Mathematical and Statistical Methods in ARtificial Intelligence, (Udine, September 1994 (G. Della Riccia, R. Kruse, and R. Viertl, eds.), Springer Verlag, pp. 199–220.
Lavrac, N., and Dzeroski, S., Inductive Logic Programming: Techniques and Applications, Ellis Horwood, Chichester, 1994.
Lavrac, N., Keravnou, E., and Zupan, B. (eds.), Intelligent Data Analysis in Medicine and Pharmacology, Kluwer Academic Publishers, 1997.
Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approach, Volume I, Tioga, Palo Alto, CA, 1983.
Michalski, R. S., Mozetic, I., Hong, J., and Lavrac, N., The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In Proc. Fifth National Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, pp. 1041–1045, 1986.
Michie, D., Spiegelhalter, D. J., and Taylor, C. C. (eds.), Machine Learning, Neural and Statistical Classification, Ellis Horwood, Chichester, 1994.
Niblett, T., and Bratko, I., Learning decision rules in noisy domains. Research and Development in Expert Systems III, (M. Bramer (ed.), Cambridge University Press, pp. 24–25, 1986.
Quinlan, J. R., Induction of decision trees. Mach. Learn.1(1):81–106, 1986.
Richeldi, M., and Rossotto, M., Class-driven statistical discretization of continuous attributes. Machine Learning: Proc. ECML-95, (N. Lavrac, and S. Wrobel(eds.), Springer Verlag, Berlin, pp. 335–342.
Zelic, I., Kononenko, I., Lavrac, N., and Vuga, V., Machine learning applied to diagnosis of sport injuries. In Proc. Artificial Intelligence in Medicine, AIME'97(Grenoble, France, April 1997), Springer Verlag, Berlin, pp. 138–141, 1997.
Zelic, I., Kononenko, I., Lavrac, N., and Vuga, V., Diagnosis of sport injuries with machine learning: Explanation of induced decisions. In Proc. Tenth IEEE Symposium on Computer-Based Medical Systems(Maribor, Slovenia, June 1997), IEEE Computer Society, Los Alamitos, CA, pp. 195–199, 1997.
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Zelič, I., Kononenko, I., Lavrač, N. et al. Induction of Decision Trees and Bayesian Classification Applied to Diagnosis of Sport Injuries. Journal of Medical Systems 21, 429–444 (1997). https://doi.org/10.1023/A:1022880431298
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DOI: https://doi.org/10.1023/A:1022880431298