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No study has compared the performance of modern machine learning techniques, against more traditional stepwise regression techniques, when developing prognostic models in individuals with CR. We analysed a prospective cohort dataset of 201 individuals with CR. Four modelling techniques (stepwise regression, least absolute shrinkage and selection operator [LASSO], boosting, and multivariate adaptive regression splines [MuARS]) were each used to form a prognostic model for each of four outcomes obtained at a 12\u00a0month follow-up (disability\u2014neck disability index [NDI]), quality of life (EQ5D), present neck pain intensity, and present arm pain intensity). For all four outcomes, the differences in mean performance between all four models were small (difference of NDI\u2009&lt;\u20091 point; EQ5D\u2009&lt;\u20090.1 point; neck and arm pain\u2009&lt;\u20092 points). Given that the predictive accuracy of all four modelling methods were clinically similar, the optimal modelling method may be selected based on the parsimony of predictors. Some of the most parsimonious models were achieved using MuARS, a non-linear technique. Modern machine learning methods may be used\u00a0to probe relationships along different regions of the predictor space.<\/jats:p>","DOI":"10.1038\/s41598-020-73740-7","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T10:03:22Z","timestamp":1602151402000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach"],"prefix":"10.1038","volume":"10","author":[{"given":"Bernard X. 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