Breast cancer diagnosis based on evolvable fuzzy classifiers and feature selection

This paper presents an architecture for evolvable fuzzy rule-based classifiers, applied to the diagnosis of breast cancer, the second most frequent cause of cancer deaths in the female population. It is based on the eClass family of relative models, having the ability to evolve its fuzzy rule-base incrementally. This incremental adaptation is gradually developed by the influence that data bring, arriving from a data stream sequentially. Recent studies have shown that the eClass algorithms are very promising solution for decision making problems. Such on-line learning method has been extensively used for control applications and is also suitable for real time classification tasks, such as fault detection, diagnosis, robotic navigation etc. We propose the use of evolvable multiple-input-multipleoutput (MIMO) Takagi Sugeno Kang (TSK) rule-based classifiers of first order, to the diagnosis of breast cancer. Moreover we introduce a novel feature scoring function that identifies most valuable features of the data in real time. Our experiments show that the algorithm returns high classification rate and the results are comparable with other approaches that regard learning from numerical observations of medical nature. © 2009 Springer-Verlag London.