embedding neonatal mortality prediction into perinatal workflows: a machine-learning approach from the iman registry

Neonatal mortality remains a major challenge in resource-limited settings, where delayed recognition of high-risk cases and inconsistent clinical decisions hinder timely and targeted interventions, which are essential for reducing preventable deaths. in response, this study developed and evaluated machine-learning models to predict neonatal death using maternal and neonatal features collected both before and after delivery. to this end, guided by the crisp-dm data-mining framework, we analyzed a dataset of 7,214 births (5,000 survivors and 2,214 deaths) from 2021–2022, derived from routinely collected records in the iranian maternal and neonatal (iman) registry. as a result, among the data-mining models—random forest, xgboost, and support vector machine—trained with imbalance-sensitive techniques, xgboost achieved the best performance (roc-auc = 0.967, pr-auc = 0.940). feature importance analysis identified gestational age (importance = 0.179) and birth weight (0.109) as the dominant predictors, followed by nervous system malformations (0.035), musculoskeletal malformations (0.033), high-risk delivery indicators (0.032), and other congenital malformations (0.031). the contribution of this study is in twofolds, first, these findings demonstrate that accurate, real-time prediction of neonatal mortality is achievable. seconds, beyomd a prognostic tool, the final model can serve as an operational lever within neonatal services; when embedded into a clinical decision support system, it can enhance early risk detection, improve triage accuracy, facilitate timely nicu preparedness, and strengthen overall process reliability and system performance in resource-limited care settings.