Authors - Mayen Ben-Koko, Emmanuel Waribo Otiti Abstract - Nigeria loses more new-borns in the first month of life than almost any other country in the world, yet no machine learning tool has been built specifically for this context. This paper proposes a framework for predicting neonatal mortality risk in Nigeria using indicators from the 2023–24 Nigeria Demographic and Health Survey — the most current national health dataset available. Five classification algorithms are compared: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine. Random Forest performed best, with an AUC-ROC of 0.89. The three strongest predictors were whether a skilled health worker attended the birth, the gap between pregnancies, and the number of antenatal visits. The framework is reproducible and designed to be extended as fuller microdata becomes available or adapted for routine clinic records across Nigeria's six geopolitical zones.
Open Zoom