| Literature DB >> 27106754 |
Sk Saddam Ahmed1, Nilanjan Dey2, Amira S Ashour3,4, Dimitra Sifaki-Pistolla5, Dana Bălas-Timar6, Valentina E Balas7, João Manuel R S Tavares8.
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.Entities:
Keywords: Backpropagation neural network; Classification; Factor analysis; Genome sequencing; Neuro-fuzzy
Mesh：
Year: 2016 PMID： 27106754 DOI： 10.1007/s11517-016-1508-7
Source DB: PubMed Journal: Med Biol Eng Comput ISSN： 0140-0118 Impact factor: 2.602