Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach.
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 diagnos...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 1; pp. 101 - 116 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120629446&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120629446 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2017 vid: 55 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120629446 120629446 NLM27106754 120629446 10.1007/s11517-016-1508-7 NLM27106754 120629446 ppf: 101 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach. aug: au: Ahmed, Sk. Dey, Nilanjan Ashour, Amira Sifaki-Pistolla, Dimitra Bălas-Timar, Dana Balas, Valentina Tavares, João Ahmed, Sk Saddam Ashour, Amira S Bălas-Timar, Dana Balas, Valentina E Tavares, João Manuel R S affil: Department of CSE , JIS College of Engineering , Kalyani India sug: subj: Neural Networks (Computer) Logic Crohn Disease Classification ROC Curve Human ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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