Interpretable machine learning models for classifying low back pain status using functional physiological variables

European spine journal

article

Bernard X. W. Liew¹  David Rugamer²  Alessandro Marco De Nunzio⁴  Deborah Falla⁵ 

[1] School of Sport, Rehabilitation and Exercise Sciences, University of Essex;Department of Statistics;School of Business and Economics, Humboldt University of Berlin;LUNEX International University of Health;Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham

关键词: Motor control;    Lifting;    Biomechanics;    Low back pain;    Machine learning;    Functional regression;   

DOI  :  10.1007/s00586-020-06356-0

来源: Springer

【 摘 要 】

To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). 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 > 0.7 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. 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 $$\beta $$   = 0.047) in model 1, the deltoid muscle (peak $$\beta $$  =  0.052) in model 2, and the iliocostalis muscle (peak  $$\beta $$ =  0.16) in model 3. 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. These slides can be retrieved under Electronic Supplementary Material.

【 授权许可】

Unknown