Data mining methods supporting diagnosis of melanoma

Abstract

Melanoma, a dangerous skin cancer, is usually diagnosed using the ABCD formula. The main objective of our research was to find a better formula resembling the original ABCD formula using four different discretization methods. All four corresponding modified ABCD formulas are significantly more accurate (with the level of significance 5%) than the original ABCD formula. Our additional objective was to calibrate the rule set induced from the original data set, describing melanoma, using the best discretization method, so that the sensitivity (the conditional probability for recognition of malignant and suspicious melanoma) was increased. This objective was accomplished using a technique of changing rule strengths.