Automatic detection of erthemato-squamous diseases using adaptive neuro- fuzzy inference systems.
A new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the ANFIS classifiers learned how to differentiate a new case in the domain. The six ANFIS classifiers were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. To improve diagnostic accuracy, the seventh ANFIS classifier (combining ANFIS) was trained using the outputs of the six ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of erythemato-squamous diseases were obtained through analysis of the ANFIS. The performances of the ANFIS model were evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has some potential in detecting the erythemato-squamous diseases. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.
Ubeyli
Elif Derya
ED
Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey.
Güler
Inan
I
D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D003951:Diagnostic Errors; D004890:Erythema / Q000175:diagnosis; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D012871:Skin Diseases / Q000145:classification / Q000175:diagnosis*; D017444:Skin Diseases, Papulosquamous / Q000175:diagnosis