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Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems
Author(s) -
Übeyli Elif Derya
Publication year - 2010
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2010.00527.x
Subject(s) - adaptive neuro fuzzy inference system , computer science , classifier (uml) , fuzzy inference system , artificial intelligence , inference , inference system , machine learning , artificial neural network , fuzzy logic , training set , diabetes mellitus , set (abstract data type) , data mining , pattern recognition (psychology) , fuzzy control system , medicine , endocrinology , programming language
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.