Abstract
Our main objective was to decrease the error rate of diagnosis of melanoma, a very dangerous skin cancer. Since diagnosticians routinely use the so-called ABCD formula for melanoma prediction, our main concern was to improve the ABCD formula. In our search for the best coefficients of the ABCD formula we used two different discretization methods, agglomerative and divisive, both based on cluster analysis. In our experiments we used the data mining system LERS (Learning from Examples based on Rough Sets). As a result of more than 30,000 experiments, two optimal ABCD formulas were found, one with the use of the agglomerative method, the other one with divisive. These formulas were evaluated using statistical methods. Our final conclusion is that it is more important to use an appropriate discretization method than to modify the ABCD formula. Also, the divisive method of discretization is better than agglomerative. Finally, diagnosis of melanoma without taking into account results of the ABCD formula is much worse, i.e., the error rate is significantly greater, comparing with any form of the ABCD formula.
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Bajcar, S., Grzymala-Busse, J. W., and Hippe. Z. S.: A comparison of six discretization algorithms used for prediction of melanoma. Accepted for the Eleventh International Symposium on Intelligent Information Systems, Poland, June 3–6, 2002.
Booker, L. B., Goldberg, D. E., and Holland J. F.: Classifier systems and genetic algorithms. In Machine Learning. Paradigms and Methods. Carbonell, J. G. (Ed.), The MIT Press, Boston, MA, 1990, 235–282.
Chmielewski, M. R. and Grzymala-Busse, J. W.: Global discretization of continuous attributes as preprocessing for machine learning. Int. Journal of Approximate Reasoning 15, 1996, 319–331.
Friedman, R. J., Rigel, D. S., and Kopf, A. W.: Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J. Clin. 35, 1985, 130–151.
Grzymala-Busse, J. P., Grzymala-Busse, J. W., and Hippe Z. S.: Melanoma prediction using data mining system LERS. Proceeding of the 25th Anniversary Annual International Computer Software and Applications Conference COMPSAC 2001, October 8–12, 2001, Chicago, IL, 615–620.
Grzymala-Busse, J. W.: LERS—A system for learning from examples based on rough sets. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Slowinski, R. (ed.), Kluwer Academic Publishers, Dordrecht, Boston, London, 1992, 3–18.
Grzymala-Busse J. W.: A new version of the rule induction system LERS. Fundamenta Informaticae 31 (1997), 27–39.
Grzymala-Busse J. W. and Hippe Z. S.: Postprocessing of rule sets induced from a melanoma data set. Accepted for the COMPSAC 2002, 26th Annual International Conference on Computer Software and Applications, Oxford, England, August 26–29, 2002.
Hippe, Z. S.: Computer database NEVI on endargement by melanoma. Task Quarterly 4, 1999, 483–488.
Holland, J. H., Holyoak, K. J., and Nisbett, R. E.: Induction. Processes of Inference, Learning, and Discovery. The MIT Press, Boston, MA, 1986.
Lorentzen, H. Weismann, K. Secher, L. Peterson, C. S. Larsen, F. G.: The dermatoscopic ABCD rule does not improve diagnostic accuracy of malignant melanoma. Acta Derm. Venereol. 79, 1999, 469–472.
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences, 11, 1982, 341–356.
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Boston, London, 1991.
Peterson, N.: Discretization using divisive cluster analysis and selected postprocessing techniques. Department of Computer Science, University of Kansas, internal report, 1993.
Stolz, W., Braun-Falco, O., Bilek, P., Landthaler, A. B., Cogneta, A. B.: Color Atlas of Dermatology, Blackwell Science Inc., Cambridge, MA, 1993.
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Grzymała-Busse, J.W., Hippe, Z.S. (2002). A Search for the Best Data Mining Method to Predict Melanoma. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_71
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DOI: https://doi.org/10.1007/3-540-45813-1_71
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