Machine Learning-Based Risk Prediction Models for Pregnancy-Related Syndromes

Pregnancy-related syndromes, such as hypertensive disorders, gestational diabetes mellitus, and preterm birth, pose a significant global health burden, affecting maternal and fetal outcomes. Traditional screening methods, reliant on isolated biomarkers or linear models, often fail to address the complex pathophysiology of these conditions.

This review synthesizes current literature on machine learning applications in obstetric care, analyzing multimodal data integration from electronic health records, biochemical markers, multi-omics, and imaging. It outlines model development workflows, including preprocessing for class imbalance (e.g., SMOTE) and interpretability tools (e.g., SHAP), while addressing ethical and technical challenges.

Ensemble methods (e.g., Random Forest, XGBoost) and deep learning (e.g., CNNs) outperform logistic regression, achieving AUC values > 0.90. Key advancements include federated learning for privacy and bias mitigation strategies to enhance generalizability across populations.

Machine learning-based models enable predictive, preventive, and personalized obstetrics, facilitating early interventions and improved perinatal outcomes, though external validation and regulatory frameworks are essential for clinical adoption.