Machine Learning-Based Predictive Models for Identifying Fetal Growth Restriction in Patients With Early-Onset Preeclampsia: Retrospective Study (Preprint)

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BACKGROUND Background: Fetal growth restriction (FGR) is a common and severe complication of early-onset preeclampsia (PE, ≤34 weeks), significantly increasing risks of perinatal mortality and morbidity. Current prediction methods lack both accuracy and clinical interpretability, which may delay interventions. OBJECTIVE Objective: This study aimed to develop and validate machine learning (ML) models to predict FGR in patients with early-onset PE using routinely available clinical parameters. METHODS Methods: We conducted a retrospective study of 711 patients with early-onset PE (n=238 with FGR, n=473 without FGR) from Fujian Maternity and Child Health Hospital (2014-2024). After rigorous variable selection using univariate analysis and LASSO regression, 8 ML algorithms including Logistic Regression (LR), Naive Bayes (NB), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP) and Elastic Network (EN) were trained on 70% of the data and validated on 30% of the data. Model performance was evaluated using sensitivity, specificity, accuracy, precision, F1-Score, Area Under the Receiver Operating Characteristic (AUROC), Area Under the Precision-Recall Curve (AUPRC) and calibration curves. Meanwhile, multivariate logistic regression was used to evaluate the independent predictive variables of each variable in the prediction model. The Shapley Additive Explanations (SHAP) method provided model interpretability. RESULTS Results: The MLP model demonstrated superior performance with AUROCs of 0.872 (training, n=158 with FGR, 31.8%) and 0.874 (validation, n=214 with FGR, 37.4%) among 8 ML models. Key predictive variables included pre-pregnancy body mass index (BMI), fundal height (FH), anemia, hyperuricemia, urinary microprotein (MAU) and fetal ultrasound biometric ratios (head circumference abdominal circumference ratio (HC/AC), umbilical artery systolic-to-diastolic ratio (UA S/D), umbilical artery blood flow pulsation index (UA PI)). Furthermore, HC/AC, BMI, UA S/D and hyperuricemia were found to be the most influential predictors in ML via SHAP. Consistent with SHAP results, similar to the results of SHAP, this study found that BMI (protective factor, OR=0.905, P=.003), HC/AC (risk factor, OR=2.372, P<.001), anemia (risk factor, OR=1.914, P=.006) and hyperuricemia (risk factor, OR=1.631, P=.028) were independent risk factors for FGR in patients with early-onset PE by multivariate logistic regression analysis. CONCLUSIONS Conclusions: Our MLP-based model accurately predicts FGR in early-onset PE patients using clinically accessible parameters. The integration of ultrasound biometric ratios and maternal biomarkers provides a practical tool for early risk stratification, with SHAP enhancing clinical interpretability for real-world application.

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