Title: Adaptive neuro-fuzzy inference system for the diagnosis of non-mechanical low back pain

Authors: Mehrdad Farzandipour; Ehsan Nabovati; Esmaeil Fakharian; Hossein Akbari; Soheila Saeedi

Addresses: Research Centre for Health Information Management, Kashan University of Medical Sciences, Kashan, Iran; Department of Health Information Management and Technology, Kashan University of Medical Sciences, Kashan, Iran ' Research Centre for Health Information Management, Kashan University of Medical Sciences, Kashan, Iran; Department of Health Information Management and Technology, Kashan University of Medical Sciences, Kashan, Iran ' Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran ' Department of Biostatistics and Public Health, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran ' Medical Informatics, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Research Centre for Health Information Management, Kashan University of Medical Sciences, Kashan, Iran

Abstract: Back pain is one of the most important causes of disability. Clinical decision support systems (CDSSs) can help physicians diagnose diseases with greater precision. This study designs and implements a CDSS to diagnose non-mechanical low back pain (LBP), including spinal brucellosis, ankylosing spondylitis, spinal tuberculosis, and spinal osteoarthritis using an adaptive neuro-fuzzy inference system (ANFIS). The highest corrected classification percentage was related to spinal brucellosis (82.8%), and CDSS was able to differentiate four non-mechanical LBP types.

Keywords: clinical decision support system; CDSS; non-mechanical low back pain; LBP; adaptive neuro-fuzzy inference system; ANFIS; diagnose.

DOI: 10.1504/IJMEI.2023.130727

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.3, pp.203 - 212

Received: 06 Nov 2020
Accepted: 16 Mar 2021
Published online: 04 May 2023 *

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