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Prediction of low back pain with two expert systems

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dc.contributor.author Sari, M.
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 01485598 (ISSN)
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/8578
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.
dc.language.iso English
dc.relation.isversionof 10.1007/s10916-010-9613-x
dc.subject Adaptive neuro-fuzzy inference system (ANFIS)
dc.subject Artificial neural network (ANN)
dc.subject Expert system
dc.subject Low back pain
dc.subject Modeling
dc.subject Skin resistance
dc.subject Visual analog scale
dc.subject adaptive neuro fuzzy inference system
dc.subject article
dc.subject artificial neural network
dc.subject controlled study
dc.subject expert system
dc.subject human
dc.subject low back pain
dc.subject major clinical study
dc.subject skin conductance
dc.subject visual analog scale
dc.subject Adult
dc.subject Aged
dc.subject Expert Systems
dc.subject Female
dc.subject Fuzzy Logic
dc.subject Hospitals, University
dc.subject Humans
dc.subject Low Back Pain
dc.subject Male
dc.subject Middle Aged
dc.subject Neural Networks (Computer)
dc.subject Pain Measurement
dc.subject Turkey
dc.title Prediction of low back pain with two expert systems
dc.type Article
dc.relation.journal Journal of Medical Systems
dc.identifier.volume 36
dc.identifier.issue 3
dc.identifier.startpage 1523
dc.identifier.endpage 1527
dc.identifier.index Scopus


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