pISSN 2288-6982
eISSN 2288-7105




Phys. Ther. Korea 2020; 27(2): 140-148

Published online May 20, 2020

© Korean Research Society of Physical Therapy

Prediction Model for the Risk of Scapular Winging in Young Women Based on the Decision Tree

Gyeong-tae Gwak1,2, BPT, PT, Sun-hee Ahn1,2, MSc, PT, Jun-hee Kim1,2, BPT, PT, Young-soo Weon1,2, BHSc, PT, Oh-yun Kwon1,3,4 , PhD, PT

1Kinetic Ergocise Based on Movement Analysis Laboratory, 2Department of Physical Therapy, The Graduate School, Yonsei University, 3Department of Physical Therapy, College of Health Science, Yonsei University, 4Department of Ergonomic Therapy, The Graduate School of Health and Environment, Yonsei University, Wonju, Korea

Correspondence to: Oh-yun Kwon

Received: January 16, 2020; Revised: February 13, 2020; Accepted: March 4, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background: Scapular winging (SW) could be caused by tightness or weakness of the periscapular muscles. Although data mining techniques are useful in classifying or predicting risk of musculoskeletal disorder, predictive models for risk of musculoskeletal disorder using the results of clinical test or quantitative data are scarce.
Objects: This study aimed to (1) investigate the difference between young women with and without SW, (2) establish a predictive model for presence of SW, and (3) determine the cutoff value of each variable for predicting the risk of SW using the decision tree method.
Methods: Fifty young female subjects participated in this study. To classify the presence of SW as the outcome variable, scapular protractor strength, elbow flexor strength, shoulder internal rotation, and whether the scapula is in the dominant or nondominant side were determined.
Results: The classification tree selected scapular protractor strength, shoulder internal rotation range of motion, and whether the scapula is in the dominant or nondominant side as predictor variables. The classification tree model correctly classified 78.79% (p = 0.02) of the training data set. The accuracy obtained by the classification tree on the test data set was 82.35% (p = 0.04).
Conclusion: The classification tree showed acceptable accuracy (82.35%) and high specificity (95.65%) but low sensitivity (54.55%). Based on the predictive model in this study, we suggested that 20% of body weight in scapular protractor strength is a meaningful cutoff value for presence of SW.

Keywords: Decision tree, Musculoskeletal disease, Physical examination