Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/8578
Title: Prediction of low back pain with two expert systems
Authors: Sarı, Murat
Gulbandilar, E.
Cimbiz, A.
Keywords: Adaptive neuro-fuzzy inference system (ANFIS)
Artificial neural network (ANN)
Expert system
Low back pain
Modeling
Skin resistance
Visual analog scale
adaptive neuro fuzzy inference system
article
artificial neural network
controlled study
expert system
human
low back pain
major clinical study
skin conductance
visual analog scale
Adult
Aged
Expert Systems
Female
Fuzzy Logic
Hospitals, University
Humans
Low Back Pain
Male
Middle Aged
Neural Networks (Computer)
Pain Measurement
Turkey
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.
URI: https://hdl.handle.net/11499/8578
https://doi.org/10.1007/s10916-010-9613-x
ISSN: 0148-5598
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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