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Medical & Biological Engineering & Computing
Erschienen in: Medical & Biological Engineering & Computing 1/2017

22.04.2016 | Original Article

Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach

verfasst von: Sk. Saddam Ahmed, Nilanjan Dey, Amira S. Ashour, Dimitra Sifaki-Pistolla, Dana Bălas-Timar, Valentina E. Balas, João Manuel R. S. Tavares

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 1/2017

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Abstract

Crohn’s disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.
Vorheriger Artikel A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms
Nächster Artikel Comparison of nonparametric methods for static visual field interpolation

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Metadaten
Titel
Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach
verfasst von
Sk. Saddam Ahmed
Nilanjan Dey
Amira S. Ashour
Dimitra Sifaki-Pistolla
Dana Bălas-Timar
Valentina E. Balas
João Manuel R. S. Tavares
Publikationsdatum
22.04.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Medical & Biological Engineering & Computing / Ausgabe 1/2017
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-016-1508-7

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