FUZZY METHODS FOR MEDICAL DIAGNOSIS
Peter R. Innocent, Robert John, Jonathan M. Garibaldi · Applied Artificial Intelligence · 2004
This paper argues that fuzzy representations are appropriate in applications where there are major sources of imprecision and/or uncertainty. Case studies of fuzzy approaches to specific problems of medical diagnosis and classification are described in support of this argument. The case studies are in the areas of categorical consistency, diagnostic monitoring, and scoring. The solutions use a variety of fuzzy methods, including clustering, fuzzy set aggregation, and type-2 fuzzy set modeling of linguistic approximations. It is concluded that the fuzzy approach to the development of artificial intelligence in application systems in beneficial in these contexts because of the need to focus on uncertainty as a main issue.