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Evolutionary fuzzy modeling human diagnostic decisions.
Journal article

Evolutionary fuzzy modeling human diagnostic decisions.

  • Peña-Reyes CA Swiss Federal Institute of Technology at Lausanne, EPFL, Logic Systems Laboratory, IN-Ecublens, CH-1015 Lausanne, Switzerland. carlos.pena@epfl.ch
  • 2004-06-23
Published in:
  • Annals of the New York Academy of Sciences. - 2004
Breast Neoplasms
Computational Biology
Diagnosis, Computer-Assisted
Female
Fuzzy Logic
Humans
Mammography
Risk Assessment
English Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/278968
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