A fuzzy-genetic approach to breast cancer diagnosis.
The automatic diagnosis of breast cancer is an important, real-world medical problem. In this 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 two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable.
Pena-Reyes
C A
CA
Logic Systems Laboratory, Swiss Federal Institute of Technology, IN-Ecublens, CH-1015, Lausanne, Switzerland. carlos.pena@di.epfl.ch
Sipper
M
M
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D016678:Genome; D006801:Humans; D008954:Models, Biological