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Explainable Machine Learning Applied to Bioelectrical Impedance for Low Back Pain: Classification and Pain-Score

Seungwan Jang1, Seung Mo Yoo2, Se Dong Min1,3

  • 1Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

Objective biomarkers for low back pain (LBP) are needed. Bioelectrical impedance parameters (BIP) analyzed with explainable machine learning show promise in identifying LBP and estimating pain intensity.

Keywords:
SHAPXGBoostbioelectrical impedanceexplainable machine learninglow back pain

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Area of Science:

  • Biomedical Engineering
  • Computational Medicine
  • Pain Research

Background:

  • Low back pain (LBP) is a leading global cause of disability.
  • Current LBP assessment relies heavily on subjective questionnaires.
  • Objective biomarkers are crucial for accurate LBP assessment.

Purpose of the Study:

  • To investigate the utility of bioelectrical impedance parameters (BIP) for objective LBP assessment.
  • To apply explainable machine learning (ML) models for LBP classification and pain intensity prediction.
  • To explore BIP as a potential objective biomarker for LBP.

Main Methods:

  • A cross-sectional study involving 83 participants (38 with LBP, 45 controls).
  • Lumbar BIP and demographic data were collected.
  • Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) was used for ML analysis.
  • Classification of LBP vs. healthy status and regression for pain scales (VAS, ODI, RMDQ) were performed.

Main Results:

  • The ML classifier achieved high accuracy in distinguishing LBP from healthy individuals (ROC-AUC = 0.996).
  • The model demonstrated strong performance in predicting Visual Analog Scale (VAS) pain scores (R² = 0.70).
  • Prediction accuracy for Oswestry Disability Index (ODI) and Roland-Morris Disability Questionnaire (RMDQ) was lower.

Conclusions:

  • Explainable ML models utilizing BIP can effectively differentiate between LBP and healthy groups.
  • BIP analysis shows potential for objective estimation of LBP intensity.
  • These findings suggest BIP as a valuable objective complement to subjective LBP assessments.