Melanoma prediction using data mining system LERS

• Published in: 25th Annual International Computer Software and Applications Conference. COMPSAC 2001 pp. 615 - 620
• Main Authors: Grzymala-Busse, P., Grzymala-Busse, J.W., Hippe, Z.S.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 2001
• Subjects: Algorithm design and analysis; Cancer; Classification algorithms; Clustering algorithms; Computer science; Data mining; Error analysis; Lesions; Malignant tumors; Systems engineering and theory
• ISBN: 0769513727; 9780769513720
• ISSN: 0730-3157
• DOI: 10.1109/CMPSAC.2001.960676

One of the important tools for early diagnosis of malignant melanoma is the total dermatoscopy score (TDS), computed using the ABCD (asymmetry, border, color, diameter) formula. Our primary objective was to check whether the ABCD formula is optimal. Using a data set containing 276 cases of melanoma and the LERS (Learning from Examples based on Rough Sets) data mining system, we checked more than 20,000 modified formulas for ABCD, computing the predicted error rate of melanoma diagnosis using 10-fold cross-validation for every modified formula. As a result, we found the optimal ABCD formula for our setup: discretization based on cluster analysis, the LEM2 (Learning from Examples Module, version 2) algorithm (one of the four LERS algorithms for rule induction) and the standard LERS classification scheme. The error rate for the standard ABCD formula was 10.21 %, while for the optimal ABCD formula the error rate was reduced to 6.04%. Some research in melanoma diagnosis shows that the use of the ABCD formula does not improve the error rate. Our research shows that the ABCD formula is useful, since, for our data set, the error rate without the use of the ABCD formula was higher (13.73%).