A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia

Abstract

Purpose: To analyze the risk factors for preterm birth in patients with early-onset preeclampsia (EOPE) based on multi-algorithm machine learning and to construct a predictive model to explore the predictive value of the model.

Methods: A retrospective analysis was conducted on 442 EOPE patients from a single tertiary center, divided into preterm birth (<37 weeks, n=358) and term-born (≥37 weeks, n=84) groups. Univariate analysis, random forest importance assessment, lasso regression combined with multivariate regression analysis were used for feature evaluation. Eight machine learning models were trained (70% data) and validated (30% data). A Stacking ensemble model was constructed, and SHapley Additive exPlanations (SHAP) was used for feature interpretation.

Results: The area under the receiver operating characteristic curve (AUROC) for predicting preterm birth in EOPE patients using Logistic Regression, Gaussian Naive Bayes, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multi-Layer Perceptron, and Elastic Net were 0.763, 0.712, 0.821, 0.832, 0.821, 0.842, 0.784, and 0.763, respectively. The Stacking model (XGBoost+GBDT+SVM) achieved superior performance (AUROC=0.865). Three independent risk factors were identified: fetal growth restriction (aOR=3.50, p = 0.047), serum cystatin C (aOR=11.27, p = 0.018), and C-reactive protein (aOR=1.37, p < 0.001). SHAP analysis revealed GBDT as the top contributor to Stacking predictions, with microalbunminuria (GBDT, XGBoost) and age (SVM) being the most influential features.

Conclusion: Machine learning models can serve as reliable assessment tools for predicting the risk of preterm birth in patients with EOPE. The ensemble prediction model demonstrates the best predictive performance, helping obstetricians identify high-risk patients and perform early intervention to improve perinatal outcomes.

Keywords: clinical prediction model; early-onset preeclampsia; machine learning; preterm birth.

Figure 1

Variable selection for the prediction model. (A) Importance ranking of feature variables in the random forest model for preterm birth risk in patients with early-onset preeclampsia. (B) Variables selected by Lasso regression, with the dashed line representing the lambda that yields the minimum mean of the target parameters, under which the model achieved the best performance. (C) Variation characteristics of LASSO regression coefficients. (D) Forest plot displaying adjusted odds ratios with 95% confidence intervals for independent risk factors associated with preterm birth in preeclampsia patients. *P<0.05.

Figure 2

Performance comparison of different machine learning models. (A) ROC curves of 9 models on the training set. (B) ROC curves of 9 models on the validation set. (C) PRC curves of 9 models on the training set. (D) PRC curves of 9 models on the validation set. (E) Heatmap of evaluation metrics for 9 models on the training set. (F) Heatmap of evaluation metrics for 9 models on the validation set.

Figure 3

SHAP value of different models on the selected feature set. (A) SHAP value of the best Stacking model. (B) SHAP value of GBDT on selected feature set. (C) SHAP value of SVM on selected feature set. (D) SHAP value of XGBoost on selected feature set.