A Stacking Ensemble Machine Learning Model for Predicting Caesarean Section Delivery in a Nigerian Cohort

The accurate identification of high-risk pregnancies requiring Caesarean section (C-section) is critical to improving maternal and neonatal outcomes. Using a retrospective dataset of 1,163 pregnant women from Yusuf Dantsoho Memorial Hospital, Kaduna, Nigeria, this study develops and validates an ensemble hybrid machine learning framework for predicting C-section deliveries. Key predictive variables include maternal age, blood pressure, placenta previa, and previous C-section history. Four models (Random Forest, AdaBoost, Gradient Boosting, and Stacking Ensemble) were evaluated using accuracy, precision, recall, and F1-score metrics. The Stacking Ensemble model achieved the highest performance (accuracy = 86.7%, recall = 0.91) and outperformed all base learners. These findings confirm the potential of ensemble learning to enhance clinical decision support for obstetricians, reduce unnecessary surgical interventions, and strengthen maternal health outcomes in resource-constrained environments. The study expands existing knowledge of predictive analytics in obstetrics by demonstrating a robust research framework for future applications and investigations.