AI-FRS: An Ensemble-Based AI Decision-Support System for Fetal Risk Prediction in a Mexican Clinical Setting

Nearly 2 million stillbirths occur globally each year. These outcomes are often driven by disparities in healthcare access, especially in low- and middle-income countries, where limited resources and shortages of trained medical personnel further increase preventable risks. Addressing these challenges requires not only strengthening healthcare systems but also enhancing intervention strategies. In this context, the development of decision-support systems becomes essential to dynamically identify at-risk pregnancies and improve fetal outcomes. Therefore, we propose AI-FRS (Artificial Intelligence–Fetal Risk Prediction System), a decision support tool for fetal risk prediction, designed to classify fetal conditions as healthy or at risk, using clinical data from Mexican obstetric patients. AI-FRS is built upon seven distinct machine learning models, systematically evaluated through 127 first-order ensemble combinations using hard voting. To further enhance predictive performance, we assessed 32,752 second-order ensembles, constructed by combining top-performing first-order ensembles across recall, precision, and F1-score metrics. The final selected model, called BSOEM, achieved a robust F1-score of 0.812, providing a more balanced and robust decision-making framework than individual models or simple ensembles. Additionally, we conducted an interpretability analysis to identify the clinical variables with the greatest contribution to model predictions, strengthening the system’s transparency and potential clinical trust. AI-FRS features a user-friendly interface specifically designed to facilitate adoption by healthcare professionals. This provides a fast and clinically applicable AI tool for intrapartum and peripartum risk detection in obstetrics, supporting clinical decision-making and improving fetal health outcomes.