A Machine Learning Framework for Preeclampsia Prediction at Isidro Ayora Hospital, Ecuador

Background: Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity, affecting 2–8% of pregnancies worldwide. In Ecuadorian public hospitals, clinical data are often fragmented, limiting the construction of complete datasets for prediction modeling. This study aimed to develop a Machine Learning (ML) framework to predict PE using demographic data from the Isidro Ayora Gynecology and Obstetrics Hospital (HGOIA), Quito, Ecuador. Methods: We proposed a custom automated Machine Learning (C-AutoML) workflow to evaluate six models (Logistic Regression, LightGBM, XGBoost, CatBoost, Multi-Layer Perceptron, and Random Forest) and compared its performance against a weighted ensemble built with Amazon SageMaker Canvas, optimizing for the F1-score. Model interpretability was analyzed using Shapley Additive Explanations (SHAP). Due to the scarcity of clinical variables at HGOIA, a synthetic dataset based on FullPIERS predictors was also generated and modeled. Results: On the hospital dataset, the SageMaker ensemble achieved an F1-score of 0.805, accuracy of 0.934, and AUC of 0.956, while the C-AutoML best single model (LightGBM) yielded comparable results (F1-score: 0.802, accuracy: 0.938, AUC: 0.944). SHAP analysis identified patient age as the most influential feature. On the synthetic dataset, Logistic Regression achieved perfect classification (F1-score: 1.0). Conclusions: The proposed C-AutoML framework matched the performance of the SageMaker weighted ensemble while relying on a single, interpretable model. Despite the current dependence on demographic data, the methodology shows promise for supporting PE diagnosis in resource-limited settings; future integration of clinical variables is expected to further enhance predictive power for risk stratification.