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Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.

Liew, Bernard XW and Kovacs, Francisco M and Rügamer, David and Royuela, Ana (2022) 'Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.' European Spine Journal. ISSN 0940-6719

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Abstract

Purpose Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when developing prognostic models in individuals with neck pain. Methods A total of 3001 participants suffering from neck pain were included into a clinical registry database. Three dichotomous outcomes of a clinically meaningful improvement in neck pain, arm pain, and disability at 3 months follow-up were used. There were 26 predictors included, five numeric and 21 categorical. Seven modelling techniques were used (logistic regression, least absolute shrinkage and selection operator [LASSO], gradient boosting [Xgboost], K nearest neighbours [KNN], support vector machine [SVM], random forest [RF], and artificial neural networks [ANN]). The primary measure of model performance was the area under the receiver operator curve (AUC) of the validation set. Results The ML algorithm with the greatest AUC for predicting arm pain (AUC=0.765), neck pain (AUC=0.726), and disability (AUC=0.703) was Xgboost. The improvement in classification AUC from stepwise logistic regression to the best performing machine learning algorithms was 0.081, 0.103, and 0.077 for predicting arm pain, neck pain, and disability, respectively. Conclusion The improvement in prediction performance between ML and logistic regression methods in the present study, could be due to the potential greater nonlinearity between baseline predictors and clinical outcome. The benefit of machine learning in prognostic modelling may be dependent on factors like sample size, variable type, and disease investigated.

Item Type: Article
Uncontrolled Keywords: Neck pain; Statistics; Prognosis; Machine learning
Divisions: Faculty of Science and Health
Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 26 Apr 2022 14:51
Last Modified: 26 Apr 2022 14:51
URI: http://repository.essex.ac.uk/id/eprint/32755

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