Predicting stillbirth and identifying key maternal risk factors using machine learning
1Statistics, Salale University, Fiche, Oromia, Ethiopia mergabdisa3@gmail.com.
Background:
Stillbirth remains a major public health concern, particularly in low-income and middle-income countries. Identifying maternal and obstetric determinants is essential for prevention and targeted interventions. Logistic regression offers a baseline predictive model, while machine learning (ML) methods, such as Random Forest (RF) and Extreme Gradient Boosting, can improve predictive accuracy and highlight key risk factors through feature importance. This study aimed to predict stillbirth and identify influential maternal and obstetric predictors among pregnant women in Ethiopia using ML models.
Methods:
A retrospective cross-sectional study was conducted using maternal and obstetric records from Bishoftu General Hospital, Ethiopia. Predictors included maternal age, weight, gravidity, gestational age at admission and delivery, history of pre-eclampsia, antenatal care visits, pregnancy complications, multiplicity, previous abortion and mode of delivery. Data were split into training (70%) and testing (30%) sets. RF, Gradient Boosting Machines, Support Vector Machines and logistic regression were applied. Model performance was evaluated using accuracy, precision, recall, balanced accuracy and receiver operating characteristic-area under the curve (ROC-AUC). Feature importance and SHapley Additive exPlanations (SHAP) supported interpretability.
Results:
Among 549 pregnancies, 17 stillbirths occurred. RF outperformed other models, achieving 92% accuracy, 0.95 ROC-AUC and 0.94 balanced accuracy. Maternal age was the strongest predictor, followed by mode of labour, maternal weight, gravidity and delivery mode. Pregnancy complications and antenatal care visits showed moderate importance, while history of pre-eclampsia, previous abortion and multiplicity contributed minimally. SHAP analysis confirmed these findings and explained variable-specific effects on risk.
Conclusions:
Maternal age emerged as the dominant determinant of stillbirth, with labour and delivery factors and maternal characteristics also contributing. ML models, particularly RF, effectively identified high-risk pregnancies and provided interpretable predictions through SHAP analysis. These findings underscore the potential of ML to support targeted prenatal care and reduce stillbirth risk in low-resource settings.
