Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6338
Title: Automatic diagnosis of diabetes using adaptive neuro-fuzzy inference systems
Authors: Übeyli, Elif Derya
Keywords: adaptive neuro-fuzzy inference system (ANFIS)
fuzzy logic
diabetes diagnosis
Issue Date: 2010
Publisher: Wiley-Blackwell
Abstract: A new approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases.
URI: https://doi.org/10.1111/j.1468-0394.2010.00527.x
https://hdl.handle.net/20.500.11851/6338
ISSN: 0266-4720
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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