Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria

Abstract

Background

Despite global efforts, disparities in antenatal care (ANC) utilization persist in Nigeria, where maternal mortality remains alarmingly high (1047 deaths per 100,000 live births). Traditional statistical models often fall short in identifying complex non-linear relationships in population health data. Machine learning (ML) offers a promising alternative that uncovers hidden patterns and improves prediction accuracy.

Methods

This study used data from the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative data set. After data preprocessing and feature selection, six supervised ML algorithms—Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost—were applied using Python 3.9. The model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). Feature importance was assessed using permutation importance and Gini impurity score.

Results

Among all models, Random Forest achieved the best performance, with 90 % accuracy, 0.90 precision and recall, an F1-score of 0.91, and an AUROC of 0.90. Permutation and Gini importance analyses identified the place of delivery, region, residence, and educational level as the most influential predictors. Other moderately important features included distance to health facilities, husband’s occupation, number of births, and healthcare decision-making autonomy—factors not highlighted by traditional statistical approaches.

Conclusion

Machine learning, particularly Random Forest, demonstrated strong predictive power in identifying the key determinants of ANC utilization. These findings highlight the potential of ML to inform targeted maternal health interventions and improve outcomes in low-resource settings, such as Nigeria.