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Titre: Generating fuzzy rules for constructing interpretable classifier of diabetes disease
Auteur(s): SETTOUTI, Nesma
CHIKH, MA.
SAIDI, Meryem
Mots-clés: Interpretable classification
Fuzzy rules
FCM
neuro-fuzzy ANFIS
UCI machine learning database
Date de publication: sep-2012
Résumé: Diabetes is a type of disease in which the body fails to regulate the amount of glucose necessary for the body. It does not allow the body to produce or properly use insulin. Diabetes has widespread fallout, with a large people affected by it in world. In this paper; we demonstrate that a fuzzy c-means-neuro-fuzzy rule-based classifier of diabetes disease with an acceptable interpretability is obtained. The accuracy of the classifier is measured by the number of correctly recognized diabetes record while its complexity is measured by the number of fuzzy rules extracted. Experimental results show that the proposed fuzzy classifier can achieve a good tradeoff between the accuracy and interpretability. Also the basic structure of the fuzzy rules which were automatically extracted from the UCI Machine learning database shows strong similarities to the rules applied by human experts. Results are compared to other approaches in the literature. The proposed approach gives more compact, interpretable and accurate classifier.
Description: Australasian Physical & Engineering Sciences in Medicine,September 2012, Volume 35, Issue 3, pp 257-270.
URI/URL: http://dspace.univ-tlemcen.dz/handle/112/1758
Collection(s) :Articles internationaux

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