Comparing the performance of machine learning techniques for low back pain diagnosis
by Hamid Bouraghi; Sorayya Rezayi; Soheila Saeedi; Rasoul Salimi; Meysam Jahani; Sajjad Abdolmaleki
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 15, No. 6, 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.

Online publication date: Fri, 27-Oct-2023

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