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Predicting pregnancy-related pelvic girdle pain using machine learning.

Atefe Ashrafi1, Daniel Thomson1, Hadi Akbarzadeh Khorshidi2

  • 1School of Health Sciences, Western Sydney University, Sydney, New South Wales, Australia.

Musculoskeletal Science & Practice
|April 18, 2025
PubMed
Summary

Machine learning models accurately predict pregnancy-related pelvic girdle pain (PPGP). A history of previous pain is the strongest predictor, offering new ways to identify at-risk women.

Keywords:
Pelvic painPredictive modellingRisk factors

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Area of Science:

  • Medical research
  • Data science in healthcare
  • Obstetrics and Gynecology

Background:

  • Pregnancy-related pelvic girdle pain (PPGP) significantly impacts pregnant women's quality of life.
  • Limited understanding exists regarding the predictability of PPGP diagnosis.
  • Identifying risk factors is crucial for timely intervention.

Purpose of the Study:

  • To compare the predictive performance of machine learning (ML) models against traditional methods for PPGP.
  • To evaluate the efficacy of various ML algorithms in predicting PPGP diagnosis.

Main Methods:

  • Reanalysis of data from 780 pregnant women at a tertiary hospital.
  • Application of ML algorithms: Logistic Regression (LR), Random Forest, XGBoost, K-Nearest Neighbors.
  • Optimization using feature selection and cross-validation; AUROC as the primary metric.

Main Results:

  • ML models, especially XGBoost and LR, showed high predictive accuracy (AUROC = 0.70).
  • Significant predictors included prior LBP/PGP history, family history, gestational age, and prolonged standing.
  • Previous LBP/PGP history was the most influential predictive factor.

Conclusions:

  • ML models demonstrate significant potential for predicting PPGP, improving risk identification.
  • Integration of ML into clinical practice can enhance early detection and preventative strategies.
  • This approach may reduce the adverse effects of PPGP on pregnant individuals.