Journal
EXPERT SYSTEMS
Volume 27, Issue 4, Pages 259-266Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1468-0394.2010.00527.x
Keywords
adaptive neuro-fuzzy inference system (ANFIS); fuzzy logic; diabetes diagnosis
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available