Hierarchical machine learning models for predicting antenatal care utilisation among Nigerian women: Identifying actionable insights for health policy

Ante-natal care (ANC) is critical for improving maternal and neo-natal health outcomes, yet utilisation remains sub-optimal in Nigeria, a country with high maternal mortality. Understanding the patterns and determinants of ANC uptake is essential for designing targeted interventions. This study employs a machine-learning-driven hierarchical modelling framework to predict ANC utilisation among Nigerian women, addressing both initial access and adequacy of care. Using a retrospective, cross-sectional design, we analysed the 2024 nationally representative Nigeria Demographic Health Survey data from 13,955 Nigerian women. ANC utilisation was categorized into three classes: no ANC (0 visits), inadequate ANC (1–7 visits), and adequate ANC (≥ 8 visits) per World Health Organisation (WHO) 2016 guidelines. A hierarchical two-stage modelling approach was implemented, with Stage 1 distinguishing any ANC from no ANC using a Light Gradient Boosting Machine (LightGBM) classifier, and Stage 2 differentiating adequate from inadequate ANC among users using an Extreme Gradient Boosting (XGBoost) classifier. Feature engineering captured socioeconomic, demographic, and geographic determinants, while threshold optimisation, calibration, and SHapley Additive exPlanations (SHAP) ensured model robustness and interpretability. Performance was evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). Of the study population, 27.90% reported no ANC, 54.52% had inadequate ANC, and only 17.58% achieved adequate ANC. Stage 1 achieved an accuracy of 0.77 with high recall (0.87) for any ANC, while Stage 2 reached an accuracy of 0.81 with improved recall (0.80) for adequate ANC after threshold optimisation. The hierarchical framework improved recall of adequate ANC from 0.50 to 0.57 in single-stage models to 0.80 at a policy-oriented operating threshold, highlighting the benefit of sequential modelling. Key predictors included socio-economic status, rural residence, and healthcare access difficulty, with SHAP analyses revealing class-specific effects. This study demonstrates the potentialof hierarchical machine learning in modelling complex ANC utilisation patterns, providing insights that could inform targeted public health strategies in Nigeria. The low proportion of women achieving adequate ANC offer actionable insights for designing targeted interventions to improve ANC coverage in Nigeria by addressing socioeconomic and geographic barriers.