ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2022
Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Factors contributing to chronic ankle instability in parcel delivery workers based on machine learning techniques.BMC medical informatics and decision making · 2025Article
- Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model.BMC oral health · 2025Article
- Development and validation of an interpretable risk prediction model for perioperative ischemic stroke in noncardiac, nonvascular, and nonneurosurgical patients: a retrospective study.Frontiers in physiology · 2025Article
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Abstract
purposePrognostic 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.
methodsA 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.
resultsThe 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.
conclusionThe 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.
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