Citation: Kim, K.H.; Choi, W.-J.;
Sohn, M.-J. Feature Importance
Analysis for Postural Deformity
Detection System Using Explainable
Predictive Modeling Technique. Appl.
Sci. 2022, 12, 925. https://doi.org/
10.3390/app12020925
Academic Editor: Keun Ho Ryu
Received: 30 November 2021
Accepted: 13 January 2022
Published: 17 January 2022
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Article
Feature Importance Analysis for Postural Deformity Detection
System Using Explainable Predictive Modeling Technique
Kwang Hyeon Kim
1,2
, Woo-Jin Choi
3
and Moon-Jun Sohn
1,2,
*
1
Department of Neurosurgery, Inje University Ilsan Paik Hospital, College of Medicine, Goyang 10380, Korea;
kh.kim@paik.ac.kr
2
Neuroscience & Radiosurgery Hybrid Research Center, Inje University, Goyang 10380, Korea
3
Allbareun Neurosurgery Clinic, Incheon 21972, Korea; k390iza@naver.com
* Correspondence: mjsohn@paik.ac.kr; Tel.: +82-31-910-7730
Abstract:
This study aimed to analyze feature importance by applying explainable artificial intel-
ligence (XAI) to postural deformity parameters extracted from a computer vision-based posture
analysis system (CVPAS). Overall, 140 participants were screened for CVPAS and enrolled. The
main data analyzed were shoulder height difference (SHD), wrist height difference (WHD), and
pelvic height difference (PHD) extracted using a CVPAS. Standing X-ray imaging and radiographic
assessments were performed. Predictive modeling was implemented with XGBoost, random forest
regressor, and logistic regression using XAI techniques for global and local feature analyses. Correla-
tion analysis was performed between radiographic assessment and AI evaluation for PHD, SHD, and
Cobb angle. Main global features affecting scoliosis were analyzed in the order of importance for
PHD (0.18) and ankle height difference (0.06) in predictive modeling. Outstanding local features were
PHD, WHD, and KHD that predominantly contributed to the increase in the probability of scoliosis,
and the prediction probability of scoliosis was 94%. When the PHD was >3 mm, the probability of
scoliosis increased sharply to 85.3%. The paired t-test result for AI and radiographic assessments
showed that the SHD, Cobb angle, and scoliosis probability were significant (p < 0.05). Feature
importance analysis using XAI to postural deformity parameters extracted from a CVPAS is a useful
clinical decision support system for the early detection of posture deformities. PHD was a major
parameter for both global and local analyses, and 3 mm was a threshold for significantly increasing
the probability of local interpretation of each participant and the prediction of postural deformation,
which leads to the prediction of participant-specific scoliosis.
Keywords: feature importance; explainable artificial intelligence; scoliosis; predictive modeling
1. Introduction
Normal spinal posture is essential for maintaining spine health and biomechanical
function including longevity. However, changes in physiological spinal curvature oc-
cur through natural aging or pathological processes because of various causes [
1
]. Early
functional changes gradually accelerate, leading to irreversible and structural spinal defor-
mation. Functional or structural spinal deformation affects spinal posture balance, causing
uneven height differences between the shoulders and pelvis, and changes the axis of nor-
mal weight distribution causing deformation that protrudes unilaterally from the coronary
plane of the spine. This imbalance in weight distribution further increases the difference in
the asymmetric height between the shoulder and pelvis, causing structural deformation
and pain in the spinal curve where pressure is concentrated [
2
,
3
]. The normal physiological
spinal curvature can be restored through functional changes by removing or improving
the factors that cause posture asymmetry. Maintaining spinal health is very important to
detect posture imbalance before spinal deformation progresses irreversibly and results
in structural spinal deformation. Specifically, early diagnosis of scoliosis should include
Appl. Sci. 2022, 12, 925. https://doi.org/10.3390/app12020925 https://www.mdpi.com/journal/applsci
