Designing breast cancer diagnostic systems via a hybrid fuzzy-genetic methodology

The automatic diagnosis of breast cancer is an important, real-world medical problem. In the paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies-fuzzy systems and evolutionary algorithms-so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting the highest classification performance shown to date, and which are also (human-)interpretable.