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<p>Hypertensive disorders of pregnancy (HDP) represent a leading cause of maternal morbidity, yet environmental chemical contributors to HDP risk remain poorly characterized within predictive frameworks. We developed and evaluated an explainable machine-learning model integrating environmental biomarkers with maternal clinical characteristics for HDP prediction using data from 4,260 pregnant women enrolled in the Korean Children’s Environmental Health Study (Ko-CHENS) prospective birth cohort. An Extreme Gradient Boosting (XGBoost) classifier incorporating 18 environmental biomarkers—including blood lead, cadmium, mercury, and endocrine-disrupting chemicals—alongside maternal clinical covariates was trained using stratified 60/20/20 data splits with Bayesian hyperparameter optimization. The optimized model achieved a cross-validated ROC-AUC of 0.787 and an independent test ROC-AUC of 0.748 (95% CI: 0.587–0.887), with a negative predictive value of 0.994 under severe class imbalance (HDP prevalence: 1.7%). SHapley Additive exPlanations (SHAP) analysis identified late-pregnancy blood lead concentration and pre-pregnancy body mass index as the dominant predictors, each exhibiting non-linear risk gradients; formal SHAP interaction values (1.6% of combined attribution) and an independent logistic-regression interaction term (β = 0.418, 95% CI − 0.278–1.115, <i>p</i> = 0.239) indicated additive rather than synergistic BMI–lead pathways. These findings demonstrate that environmental biomarkers contribute independent, non-linear predictive value beyond established clinical risk factors, supporting integration of environmental exposure surveillance into precision obstetric risk stratification.</p>

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Explainable machine learning integrating environmental chemical biomarkers and maternal clinical factors for prediction of hypertensive disorder of pregnancy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Changwon Wang,
  • Jong-Dae Lee,
  • Ju Hee Kim

摘要

Hypertensive disorders of pregnancy (HDP) represent a leading cause of maternal morbidity, yet environmental chemical contributors to HDP risk remain poorly characterized within predictive frameworks. We developed and evaluated an explainable machine-learning model integrating environmental biomarkers with maternal clinical characteristics for HDP prediction using data from 4,260 pregnant women enrolled in the Korean Children’s Environmental Health Study (Ko-CHENS) prospective birth cohort. An Extreme Gradient Boosting (XGBoost) classifier incorporating 18 environmental biomarkers—including blood lead, cadmium, mercury, and endocrine-disrupting chemicals—alongside maternal clinical covariates was trained using stratified 60/20/20 data splits with Bayesian hyperparameter optimization. The optimized model achieved a cross-validated ROC-AUC of 0.787 and an independent test ROC-AUC of 0.748 (95% CI: 0.587–0.887), with a negative predictive value of 0.994 under severe class imbalance (HDP prevalence: 1.7%). SHapley Additive exPlanations (SHAP) analysis identified late-pregnancy blood lead concentration and pre-pregnancy body mass index as the dominant predictors, each exhibiting non-linear risk gradients; formal SHAP interaction values (1.6% of combined attribution) and an independent logistic-regression interaction term (β = 0.418, 95% CI − 0.278–1.115, p = 0.239) indicated additive rather than synergistic BMI–lead pathways. These findings demonstrate that environmental biomarkers contribute independent, non-linear predictive value beyond established clinical risk factors, supporting integration of environmental exposure surveillance into precision obstetric risk stratification.