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https://hdl.handle.net/11499/8578
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sarı, Murat | - |
dc.contributor.author | Gulbandilar, E. | - |
dc.contributor.author | Cimbiz, A. | - |
dc.date.accessioned | 2019-08-16T12:42:49Z | |
dc.date.available | 2019-08-16T12:42:49Z | |
dc.date.issued | 2012 | - |
dc.identifier.issn | 0148-5598 | - |
dc.identifier.uri | https://hdl.handle.net/11499/8578 | - |
dc.identifier.uri | https://doi.org/10.1007/s10916-010-9613-x | - |
dc.description.abstract | Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation. © Springer Science+Business Media, LLC 2010. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Journal of Medical Systems | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Adaptive neuro-fuzzy inference system (ANFIS) | en_US |
dc.subject | Artificial neural network (ANN) | en_US |
dc.subject | Expert system | en_US |
dc.subject | Low back pain | en_US |
dc.subject | Modeling | en_US |
dc.subject | Skin resistance | en_US |
dc.subject | Visual analog scale | en_US |
dc.subject | adaptive neuro fuzzy inference system | en_US |
dc.subject | article | en_US |
dc.subject | artificial neural network | en_US |
dc.subject | controlled study | en_US |
dc.subject | expert system | en_US |
dc.subject | human | en_US |
dc.subject | low back pain | en_US |
dc.subject | major clinical study | en_US |
dc.subject | skin conductance | en_US |
dc.subject | visual analog scale | en_US |
dc.subject | Adult | en_US |
dc.subject | Aged | en_US |
dc.subject | Expert Systems | en_US |
dc.subject | Female | en_US |
dc.subject | Fuzzy Logic | en_US |
dc.subject | Hospitals, University | en_US |
dc.subject | Humans | en_US |
dc.subject | Low Back Pain | en_US |
dc.subject | Male | en_US |
dc.subject | Middle Aged | en_US |
dc.subject | Neural Networks (Computer) | en_US |
dc.subject | Pain Measurement | en_US |
dc.subject | Turkey | en_US |
dc.title | Prediction of low back pain with two expert systems | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 36 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.startpage | 1523 | |
dc.identifier.startpage | 1523 | en_US |
dc.identifier.endpage | 1527 | en_US |
dc.authorid | 0000-0003-0508-2917 | - |
dc.identifier.doi | 10.1007/s10916-010-9613-x | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.pmid | 20978929 | en_US |
dc.identifier.scopus | 2-s2.0-84864045305 | en_US |
dc.identifier.wos | WOS:000303826000048 | en_US |
dc.identifier.scopusquality | Q3 | - |
dc.owner | Pamukkale University | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairetype | Article | - |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 17.04. Mathematics | - |
Appears in Collections: | Fen-Edebiyat Fakültesi Koleksiyonu PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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