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A Hybrid Artificial Immune Genetic Algorithm with Fuzzy Rules for Breast Cancer Diagnosis

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we give an introduction to fuzzy systems, genetic algorithms and artificial immune system, and then we introduce a hybrid algorithm that gathers the genetic algorithms with the artificial immune system in one algorithm. The genetic algorithm, the artificial immune system and the hybrid algorithm were implemented and tested on the Wisconsin breast cancer diagnosis (WBCD) problem in order to generate a fuzzy rule system for breast cancer diagnosis. The hybrid algorithm generated a fuzzy system which reached the maximum classification ratio earlier than the two other ones. The motivations of using fuzzy rules incorporate with evolutionary algorithms in the underline problem are attaining high classification performance with the possibility of attributing a confidence measure (degree of benignity or malignancy) to the output diagnosis beside the simplicity of the diagnosis system which means that the system is human interpretable.