Predictors of High Fertility in Nigerian Women Aged 40-49: Insights from Machine Learning and Explainable Artificial Intelligence Using the 2024 Nigeria Demographic Health Survey.

Abstract

Background: This study aim to identify key determinants of high fertility and enhance model interpretability for both population-level and individual-level predictions.

Methods: This cross-sectional analytic study utilized data from the 2024 Nigeria Demographic and Health Survey, focusing on 7370 women aged 40-49 years across Nigeria's six geopolitical zones, of whom 4089 (55.5%) were classified as high fertility (parity ≥5) and 3281 (44.5%) as low fertility (parity 0-4). Multiple machine learning algorithms, including logistic regression, stochastic gradient descent (SGD), random forest, gradient boosting, and XGBoost, were evaluated for predictive performance using metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC). A decision threshold of 0.45 was applied to optimize sensitivity for high fertility classification. An explainable artificial intelligence framework incorporating SHapley Additive explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) was used to elucidate feature contributions and ensure transparency.

Results: Gradient boosting achieved the highest accuracy (76.9%) and recall for high fertility (0.845) under the optimized threshold, while SGD excelled in sensitivity (recall: 0.857). All models demonstrated strong discriminatory ability (ROC-AUC: 0.827-0.840). SHAP and LIME analyses identified ideal family size, ethnicity, education, contraceptive use, and region as principal predictors of high fertility. Women with no formal education (adjusted odds ratio [AOR]: 2.31; 95% confidence interval [CI]: 1.79-2.98) and those in the Northwest (AOR: 2.20; 95% CI: 1.67-2.91) faced significantly higher odds of high parity, while urban residence (AOR: 0.83; 95% CI: 0.72-0.95) and internet use (AOR: 0.69; 95% CI: 0.58-0.82) were protective.

Conclusions: These findings underscore the critical role of sociocultural and economic factors in driving high fertility in Nigeria, offering evidence for targeted reproductive health interventions. Policymakers can prioritize education and digital access initiatives, particularly in high-risk regions like the Northwest and Northeast, to reduce fertility-related health risks.