Learning from examples based on rough multisets
Proceedings of the Second International Symposium on Methodologies for intelligent systems
C4.5: programs for machine learning
C4.5: programs for machine learning
A new version of the rule induction system LERS
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Postprocessing of Rule Sets Induced from a Melanoma Data Set
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
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
Melanoma Prediction Using Data Mining System LERS
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Image recognition system for diagnosis support of melanoma skin lesion
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
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A parameter called TDS (Total Dermatoscopic Score), calculated by the well-known ABCD formula, is frequently used in melanoma diagnosis. In our previous research we found a new formula, similar to the original ABCD formula, that yielded fewer diagnostic errors. This new ABCD formula was developed using data mining techniques, in particular, the rule induction algorithm LEM2, a part of the data mining system LERS. In this paper we compare the quality of the old and new ABCD formulas, measured by the number of diagnostic errors, using three other data mining techniques: two rule induction algorithms, LEM1 and a modified version of LEM2 called MLEM2, and the decision tree generating system C4.5. Additionally, we compare the quality of diagnosis using TDS (original and new) and diagnosis without using TDS at all, to address complaints by some diagnosticians that TDS does not improve diagnosis of melanoma. Our experiments show that TDS is a valuable tool, significantly increasing melanoma diagnosis accuracy.