Musculoskeletal science & practice · 2025
Predicting pregnancy-related pelvic girdle pain using machine learning.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Ashrafi Atefe, Thomson Daniel, Khorshidi Hadi Akbarzadeh, Marashi Amir, Beales Darren, Ceprnja Dragana, Gupta Amitabh
Аннотация
BACKGROUND: Pregnancy-related pelvic girdle pain (PPGP) is a common complication during gestation which negatively influences pregnant women's quality of life. There are numerous risk factors associated with PPGP, however, there is limited information about being able to predict the diagnosis of PPGP.
OBJECTIVE: To compare machine learning (ML) and traditional predictive modelling to predict the clinical diagnosis of PPGP.
METHODS: This study reanalysed data from 780 pregnant women attending a tertiary hospital. ML algorithms, including Logistic Regression (LR), Random Forest, Xtreme Gradient Boost (XGBoost), and K-Nearest Neighbors, were used to predict the clinical diagnosis of PPGP. Feature selection methods and cross-validation were employed to optimize model performance, with the Area Under the Receiver Operating Characteristic Curve (AUROC) as the primary outcome measure.
RESULTS: The ML models, particularly XGBoost and LR, demonstrated high levels of predictive accuracy (AUROC = 0.70). Key predictive factors were a history of low back pain/pelvic girdle pain (LBP/PGP) in previous pregnancies, family history, gestational age, and a longer duration of standing during the day. The history of LBP/PGP in previous pregnancies emerged as the most significant predictor.
CONCLUSIONS: This study highlighted the potential of ML models to enhance the ability to predict PPGP and offers a more accurate and comprehensive approach to identifying women at risk of PPGP. The integration of ML techniques into clinical practice could improve early identification and inform preventative and intervention strategies, potentially reducing the impact of PPGP on pregnant women.
OBJECTIVE: To compare machine learning (ML) and traditional predictive modelling to predict the clinical diagnosis of PPGP.
METHODS: This study reanalysed data from 780 pregnant women attending a tertiary hospital. ML algorithms, including Logistic Regression (LR), Random Forest, Xtreme Gradient Boost (XGBoost), and K-Nearest Neighbors, were used to predict the clinical diagnosis of PPGP. Feature selection methods and cross-validation were employed to optimize model performance, with the Area Under the Receiver Operating Characteristic Curve (AUROC) as the primary outcome measure.
RESULTS: The ML models, particularly XGBoost and LR, demonstrated high levels of predictive accuracy (AUROC = 0.70). Key predictive factors were a history of low back pain/pelvic girdle pain (LBP/PGP) in previous pregnancies, family history, gestational age, and a longer duration of standing during the day. The history of LBP/PGP in previous pregnancies emerged as the most significant predictor.
CONCLUSIONS: This study highlighted the potential of ML models to enhance the ability to predict PPGP and offers a more accurate and comprehensive approach to identifying women at risk of PPGP. The integration of ML techniques into clinical practice could improve early identification and inform preventative and intervention strategies, potentially reducing the impact of PPGP on pregnant women.