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Diagnosis of Mechanical Low Back Pain Using a Fuzzy Logic-Based Approach Publisher



Fakharian E1 ; Nabovati E2 ; Farzandipour M3 ; Akbari H4 ; Saeedi S5
Authors
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Authors Affiliations
  1. 1. Research Centre for Trauma, Kashan University of Medical Sciences, Kashan, Iran
  2. 2. Research Centre for Health Information Management, Department of Health Information Management & Technology, School of Allied Health Professions, Kashan University of Medical Sciences, Kashan, Iran
  3. 3. Research Centre for Health Information Management, Department of Health Information Management & Technology, Kashan University of Medical Sciences, Kashan, Iran
  4. 4. Research Centre for Social Determinants of Health (SDH), School of Health, Kashan University of Medical Sciences, Kashan, Iran
  5. 5. Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Source: International Journal of Intelligent Systems and Applications in Engineering Published:2021


Abstract

Back pain is one of the main causes of disability, and its proper diagnosis and treatment are difficult tasks. Intelligent methods can help physicians make a more precise diagnosis of diseases. The present study was conducted to diagnose the correct type of mechanical low back pain (LBP) using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The diagnostic parameters of mechanical LBP were determined using library reviews and the views of experts based on the Delphi technique. Modelling was done in MATLAB R2012 using the ANFIS. After the modelling stage, the method was tested in terms of the percentage of corrected classification and diagnostic value indicators. Modelling is applied in the present study to diagnose different types of mechanical LBP, including back strain, spondylolisthesis, spinal stenosis, disc herniation, and scoliosis. The modelling input included 17 diagnostic parameters, and its output contained various types of mechanical LBP. The percentage of corrected classification varied from 80.9% to 83.8% (disc herniation and spondylolisthesis). The system test in the present study showed an appropriate accuracy in diagnosing different types of mechanical LBP. As a result, this system can be helpful in clinical settings for diagnosing different types of mechanical LBP presenting with similar symptoms. © 2021, Ismail Saritas. All rights reserved.
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