A Search for the Best Data Mining Method to Predict Melanoma
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Data mining based on rough sets
Data mining
Diagnosis of melanoma based on data mining and ABCD formulas
Design and application of hybrid intelligent systems
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Rule induction based on an incremental rough set
Expert Systems with Applications: An International Journal
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Melanoma recognition using representative and discriminative kernel classifiers
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Computer---Aided diagnosis of pigmented skin dermoscopic images
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
International Journal of Software Science and Computational Intelligence
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One of the important tools for early diagnosis of malignant melanoma is the total dermatoscopy score computed using the ABCD formula. Our primary objective was to check whether the well-known ABCD formula is optimal. Using a data set containing 276 cases of melanoma and the LERS data mining system we checked more than 20,000 modified formulas for ABCD, computing the predicted error rate of melanoma diagnosis using ten-fold cross validation for every modified formula. As a result we found the optimal ABCD formula for our setup: discretization based on cluster analysis, LEM2 algorithm for rule induction (one of the four LERS algorithms for rule induction), and 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%).