Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis

Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis Publisher

Bouraghi H¹ ; Rezayi S² ; Saeedi S^{2, 3} ; Salimi R⁴ ; Jahani M⁵ ; Abdolmaleki S⁶

Show Affiliations

1. Department of Health Information Technology, School of Allied Medical Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
2. Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3. Clinical Research Development Unit of Farshchian Heart Centre, Hamadan University of Medical Sciences, Hamadan, Iran
4. Emergency Department, Besat Hospital, Hamadan University of Medical Sciences, Hamadan, Iran
5. Department of Computer Engineering, Faculty of Technology and Engineering, University of Isfahan, Isfahan, Iran
6. Department of Neurosurgery, Hamadan University of Medical Sciences, Hamadan, Iran

Source: International Journal of Medical Engineering and Informatics Published:2023

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. © 2023 Inderscience Enterprises Ltd.

Related Docs

View other Related Docs

1. The Quality of Care Index for Low Back Pain: A Systematic Analysis of the Global Burden of Disease Study 1990–2017, Archives of Public Health (2023)

2. Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk, Journal of Advances in Medical and Biomedical Research (2021)

3. Global, Regional, and National Burden of Low Back Pain, 1990–2020, Its Attributable Risk Factors, and Projections to 2050: A Systematic Analysis of the Global Burden of Disease Study 2021, The Lancet Rheumatology (2023)

4. The Global Spine Care Initiative: World Spine Care Executive Summary on Reducing Spine-Related Disability in Low- and Middle-Income Communities, European Spine Journal (2018)

5. Identifying Determinants for Falls Among Iranian Older Adults: Insights From the Bushehr Elderly Health Program, BMC Geriatrics (2024)

6. The Global Spine Care Initiative: Model of Care and Implementation, European Spine Journal (2018)

7. Prediction of Breast Cancer Using Machine Learning Approaches, Journal of Biomedical Physics and Engineering (2022)

8. Effect of Multidimensional Physiotherapy on Non-Specific Chronic Low Back Pain: A Randomized Controlled Trial, Advances in Rheumatology (2023)

Experts (# of related papers)

View all Related Experts

Alireza Atashi (1)

Reza Malekzadeh (1)

Style	Citing Format
MLA	Bouraghi H, et al.. "Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis." International Journal of Medical Engineering and Informatics, vol. 15, no. 6, 2023, pp. 564-572.
APA	Bouraghi H, Rezayi S, Saeedi S, Salimi R, Jahani M, Abdolmaleki S (2023). Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis. International Journal of Medical Engineering and Informatics, 15(6), 564-572.
Chicago	Bouraghi H, Rezayi S, Saeedi S, Salimi R, Jahani M, Abdolmaleki S. "Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis." International Journal of Medical Engineering and Informatics 15, no. 6 (2023): 564-572.
Harvard	Bouraghi H et al. (2023) 'Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis', International Journal of Medical Engineering and Informatics, 15(6), pp. 564-572.
Vancouver	Bouraghi H, Rezayi S, Saeedi S, Salimi R, Jahani M, Abdolmaleki S. Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis. International Journal of Medical Engineering and Informatics. 2023;15(6):564-572.
BibTex	@article{ author = {Bouraghi H and Rezayi S and Saeedi S and Salimi R and Jahani M and Abdolmaleki S}, title = {Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis}, journal = {International Journal of Medical Engineering and Informatics}, volume = {15}, number = {6}, pages = {564-572}, year = {2023} }
RIS	TY - JOUR AU - Bouraghi H AU - Rezayi S AU - Saeedi S AU - Salimi R AU - Jahani M AU - Abdolmaleki S TI - Comparing the Performance of Machine Learning Techniques for Low Back Pain Diagnosis JO - International Journal of Medical Engineering and Informatics VL - 15 IS - 6 SP - 564 EP - 572 PY - 2023 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract