Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/8578
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dc.contributor.authorSarı, Murat-
dc.contributor.authorGulbandilar, E.-
dc.contributor.authorCimbiz, A.-
dc.date.accessioned2019-08-16T12:42:49Z
dc.date.available2019-08-16T12:42:49Z
dc.date.issued2012-
dc.identifier.issn0148-5598-
dc.identifier.urihttps://hdl.handle.net/11499/8578-
dc.identifier.urihttps://doi.org/10.1007/s10916-010-9613-x-
dc.description.abstractLow 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.isoenen_US
dc.relation.ispartofJournal of Medical Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive neuro-fuzzy inference system (ANFIS)en_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectExpert systemen_US
dc.subjectLow back painen_US
dc.subjectModelingen_US
dc.subjectSkin resistanceen_US
dc.subjectVisual analog scaleen_US
dc.subjectadaptive neuro fuzzy inference systemen_US
dc.subjectarticleen_US
dc.subjectartificial neural networken_US
dc.subjectcontrolled studyen_US
dc.subjectexpert systemen_US
dc.subjecthumanen_US
dc.subjectlow back painen_US
dc.subjectmajor clinical studyen_US
dc.subjectskin conductanceen_US
dc.subjectvisual analog scaleen_US
dc.subjectAdulten_US
dc.subjectAgeden_US
dc.subjectExpert Systemsen_US
dc.subjectFemaleen_US
dc.subjectFuzzy Logicen_US
dc.subjectHospitals, Universityen_US
dc.subjectHumansen_US
dc.subjectLow Back Painen_US
dc.subjectMaleen_US
dc.subjectMiddle Ageden_US
dc.subjectNeural Networks (Computer)en_US
dc.subjectPain Measurementen_US
dc.subjectTurkeyen_US
dc.titlePrediction of low back pain with two expert systemsen_US
dc.typeArticleen_US
dc.identifier.volume36en_US
dc.identifier.issue3en_US
dc.identifier.startpage1523
dc.identifier.startpage1523en_US
dc.identifier.endpage1527en_US
dc.authorid0000-0003-0508-2917-
dc.identifier.doi10.1007/s10916-010-9613-x-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.pmid20978929en_US
dc.identifier.scopus2-s2.0-84864045305en_US
dc.identifier.wosWOS:000303826000048en_US
dc.identifier.scopusqualityQ3-
dc.ownerPamukkale University-
item.languageiso639-1en-
item.openairetypeArticle-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept17.04. 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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