Research Repository

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

Liew, Bernard XW and Rugamer, David and De Nunzio, Alessandro Marco and Falla, Deborah (2020) 'Interpretable machine learning models for classifying low back pain status using functional physiological variables.' European Spine Journal, 29 (8). pp. 1845-1859. ISSN 0940-6719

[img]
Preview
Text
Interpretable_machine_learning_models_for_classify.pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview

Abstract

PURPOSE: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. METHODS: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. RESULTS: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 [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION: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.

Item Type: Article
Uncontrolled Keywords: Motor control; Lifting; Biomechanics; Low back pain; Machine learning; Functional regression
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: 24 Nov 2020 15:07
Last Modified: 06 Jan 2022 14:11
URI: http://repository.essex.ac.uk/id/eprint/28945

Actions (login required)

View Item View Item