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Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of &gt;\u20090.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\beta $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mi>\u03b2<\/mml:mi>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u2009 =\u20090.047) in model 1, the deltoid muscle (peak <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\beta $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mi>\u03b2<\/mml:mi>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u2009=\u2009\u20090.052) in model 2, and the iliocostalis muscle (peak\u2009<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\beta $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mi>\u03b2<\/mml:mi>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula> =\u2009\u00a00.16) in model 3.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Graphic abstract<\/jats:title>\n<jats:p>These slides can be retrieved under Electronic Supplementary Material.<\/jats:p>\n<\/jats:sec>","DOI":"10.1007\/s00586-020-06356-0","type":"journal-article","created":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T19:02:47Z","timestamp":1583175767000},"page":"1845-1859","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Interpretable machine learning models for classifying low back pain status using functional physiological variables"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7057-7548","authenticated-orcid":false,"given":"Bernard X. 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