@INPROCEEDINGS { Rsds2731,
author = {Busse-Grzymala, Jerzy W. and Hippe, Zdzislaw S.}, 
title = {A Search for the Best Data Mining Method to Predict Melanoma}, 
booktitle = {Rough Sets and Current Trends in Computing}, 
conference = {Rough Sets and Current Trends in Computing}, 
series =  = {Lecture Notes in Arificial Intelligence}, 
volume = 2475, 
pages = {538--545}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {October}, 
year = 2002, 
editor = {Alpigini, James J. and Peters, James F. and Skowron, Andrzej and Zhong, Ning Liu Jiming}, 
issn = {0302-9743}, 
isbn = {3-540-44274-X}, 
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.}, 
keywords = {rough set theory (RST), data mining, melanoma prediction, ABCD formula, discretization, }, 
}