Machine learning/AI for early neonatal complication detection in rural Ethiopia: A retrospective cohort study in the Sidama region.

Introduction: Neonatal complications remain a leading cause of illness and death in low- and middle-income countries, particularly in rural areas. Early identification of high-risk neonates is crucial for timely interventions. This study assessed the incidence and determinants of neonatal complications and evaluated the predictive performance of machine learning algorithms using a unified risk framework encompassing both adverse birth outcomes and early postnatal complications. Methods: We conducted a retrospective cohort study using routinely collected maternal and neonatal records. Five supervised machine learning models - logistic regression (LR), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were developed in R. Model performance was assessed with area under the curve (AUC), sensitivity, specificity, F1 score, and calibration. SHapley Additive Explanations (SHAP) identified key predictors. Sensitivity analyses evaluated the robustness of results by examining birth outcomes and postnatal complications separately. Results: Of the neonates studied, 15.2% (95% CI: 14.0-16.5) experienced complications, with higher rates in rural (17.1%) than urban areas (11.2%, Conclusion: Neonatal complications remain prevalent, with pronounced rural-urban disparities. XGBoost offers accurate and interpretable early risk prediction using routine maternal and antenatal data. Targeted interventions including expanded prenatal care, anemia management, and strengthened rural health services could reduce neonatal morbidity and mortality.