Title: Comparing the performance of machine learning techniques for low back pain diagnosis

Authors: Hamid Bouraghi; Sorayya Rezayi; Soheila Saeedi; Rasoul Salimi; Meysam Jahani; Sajjad Abdolmaleki

Addresses: Department of Health Information Technology, School of Allied Medical Sciences, Hamadan University of Medical Sciences, Hamadan, Iran ' Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ' Clinical Research Development Unit of Farshchian Heart Centre, Hamadan University of Medical Sciences, Hamadan, Iran; Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ' Emergency Department, Besat Hospital, Hamadan University of Medical Sciences, Hamadan, Iran ' Department of Computer Engineering, Faculty of Technology and Engineering, University of Isfahan, Isfahan, Iran ' Department of Neurosurgery, Hamadan University of Medical Sciences, Hamadan, Iran

Abstract: Low back pain is a global health problem that is a major cause of disability in developing and developed countries. Machine learning and data mining algorithms can be used to help diagnose this disease. This study aimed to determine the performance of different machine learning algorithms. Nine machine learning techniques, including support vector machine, decision tree, Naive Bayes, K-nearest neighbours, neural network, random forest, deep learning, auto-MLP, and rule induction, were used to modelling. This study revealed that the highest accuracy was related to the random forest (83.55%) and support vector machine (82.26%) classifiers. As a result, machine learning algorithms have good accuracy in low back pain diagnosis.

Keywords: machine learning; data mining; low back pain; LBP; diagnose.

DOI: 10.1504/IJMEI.2023.134535

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.6, pp.564 - 572

Received: 19 May 2021
Accepted: 12 Sep 2021
Published online: 27 Oct 2023 *

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