Optimization of the ABCD Formula for Melanoma Diagnosis Using C4.5, a Data Mining System

Using C4.5, a Data Mining System Ron Andrews Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA Stanislaw Bajcar Regional Dermatology Center, 35-310 Rzeszow, Poland Jerzy W. Grzymala-Busse Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA and Institute of Computer Science Polish Academy of Sciences, 01-237 Warsaw, Poland Zdzislaw S. Hippe Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225 Rzeszow, Poland Chris Whiteley Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA Abstract. Our main objective was to improve the diagnosis of melanoma by optimizing the ABCD formula, used by dermatologists in melanoma identification. In our previous research, an attempt to optimize the ABCD formula using the LEM2 rule induction algorithm was successful. This time we decided to replace LEM2 by C4.5, a tree generating data mining system. The final conclusion is that, most likely, for C4.5 the original ABCD formula is already optimal and no further improvement is possible.