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Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

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Abstract:

In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.
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Keywords: Behcet disease; Doppler signal; adaptive neuro-fuzzy inference system; fast Fourier transform; ophthalmic artery

Document Type: Research Article

Affiliations: 1: Department of Electrical and Electronics Engineering, Faculty of Engineering, Kirikkale University, Kirikkale, Turkey 2: Department of Computer Engineering, Faculty of Engineering, Kirikkale University, Kirikkale, Turkey ; [email protected], Email: [email protected]

Publication date: May 1, 2007

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