Predicting birth asphyxia in newborns via supervised machine learning: a cross sectional study in Tigray, Ethiopia 2025

Abstract

Background: Birth asphyxia, a critical condition characterized by insufficient oxygen supply to a newborn before, during, or after birth, is the second leading cause of neonatal mortality in Ethiopia. It contributes substantially to preventable neonatal morbidity and long-term neurodevelopmental impairment. The burden is especially high in low-resource regions like Tigray, where healthcare systems have been severely impacted by conflict and limited infrastructure. Early and accurate prediction of at-risk newborns is essential, and supervised machine learning (ML) offers a powerful data-driven solution to support clinical decision-making. Objective: To predict birth asphyxia in newborns using supervised machine learning: a cross sectional study in Tigray, Ethiopia (2025). Methods: An institution-based prospective study was conducted among 1014 mothers and their newborns who delivered at four selected hospitals in Tigray (Ayder, Mekelle, Quiha, and Wukro) between February 25 and April 10, 2025. A convenience sampling technique was used to recruit eligible participants. The dataset underwent thorough preprocessing, including handling missing values, one-hot encoding, normalization, hybrid feature selection approach, and class balancing. Seven ML models—logistic regression, support vector machine, decision tree, random forest (RF), naive bayes, k-nearest neighbors, and extreme gradient boosting were trained and evaluated. The data were split into 80% for training and 20% for testing, with model performance assessed using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (AUC) with 95% confidence intervals. Shapley Additive Explanations was employed for model interpretability, validated across cross-validation folds. Results: Of the 1014 neonates included, 195 (19.2%) were diagnosed with birth asphyxia based on APGAR scores and physician confirmation. The random forest classifier achieved the best performance, with an AUC of 0.99 (95% CI: 0.98–1.00) and Brier score of 0.0099 (95% CI: 0.008–0.012). SHAP analysis identified fetal heart rate (38.6%), birth weight (11.2%), mal-presentation (8.1%), hypothermia (7.7%), referral status (7.5%), and prolonged labor (6.5%) are collectively contributing 79.6% to the model’s predictive capacity, consistent across folds (standard deviation of SHAP values <0.02). Conclusion: The RF model demonstrated excellent performance in predicting birth asphyxia and offered strong interpretability. Nearly 80% of the model's predictive power was explained by a small number of clinically actionable variables. These findings support the integration of interpretable machine learning tools into routine labor management to reduce birth asphyxia. Future external validation and deployment as a web-based tool are planned.