Research Article
Determinants of Higher Fertility Rate of Married Women in Rural Nigeria
Salome Amarachi Ike-Wegbom
,
Anthony Ike Wegbom*,
Adolphus Okechukwu Nwaoburu
Issue:
Volume 10, Issue 3, September 2025
Pages:
56-63
Received:
20 August 2025
Accepted:
2 September 2025
Published:
25 September 2025
Abstract: Background: Nigeria’s fertility rate remains high at 5.3 births per woman, with rural areas recording even higher rates, largely due to early marriage, low contraceptive use, and limited female education. This study identifies the factors associated with a higher number of children ever born among married women in rural Nigeria. Methods: This study extracted data from the 2018 Nigeria Demographic and Health Survey (NDHS). A binary logistic regression model was employed to assess the determinants of higher fertility rates among married women, with statistical significance set at p ≤ 0.05 and a 95% confidence interval not including unity. Results: The findings revealed that 66.4% of respondents had more children, while 33.6% had fewer children. The factors significantly associated with higher fertility included maternal age, age at first birth, contraceptive use, desire for more children, and level of education. Women aged 30-39 were significantly more likely to have a higher child (aOR = 444.02; 95% CI: 210.37-937.18) than those aged 15-19. An early age at first birth was linked to increased fertility, while contraceptive use and higher educational attainment were associated with fewer children. Additionally, women residing in rural northern Nigeria exhibited higher fertility levels than those in the southern regions. Conclusion: The study highlights a high fertility rate among married women in rural Nigeria and the influence of sociodemographic factors. There is a need to focus on girl-child education, discourage early marriage, and expand access to contraceptive services, especially in rural northern Nigeria. Stakeholders should implement a broad public awareness campaign on smaller family sizes' health, economic and social benefits.
Abstract: Background: Nigeria’s fertility rate remains high at 5.3 births per woman, with rural areas recording even higher rates, largely due to early marriage, low contraceptive use, and limited female education. This study identifies the factors associated with a higher number of children ever born among married women in rural Nigeria. Methods: This study...
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Research Article
An Explainable AI Framework for Neonatal Mortality Risk Prediction in Kenya: Enhancing Clinical Decisions with Machine Learning
Issue:
Volume 10, Issue 3, September 2025
Pages:
64-83
Received:
21 August 2025
Accepted:
30 August 2025
Published:
30 September 2025
Abstract: Neonatal mortality remains a critical public health challenge in Kenya, with a rate of 21 per 1,000 live births—well above the SDG 3.2 target. While machine learning (ML) offers potential for risk prediction, most models lack transparency and clinical interpretability, limiting their adoption in low-resource settings. This study presents an explainable AI (XAI) framework for predicting neonatal mortality using Kenya Demographic and Health Survey (KDHS) data (N = 2,000), with a focus on model accuracy, fairness, and clinical relevance. Six ML models—Logistic Regression (LR), KNN, SVM, Naïve Bayes, Random Forest, and XG-Boost—were trained and evaluated using in-sample, out-of-sample, and balanced datasets, with performance assessed via AUC, F1-score, sensitivity, specificity, and Cohen’s Kappa. To address class imbalance and enhance generalizability, synthetic oversampling and rigorous cross-validation were applied. Post-balancing, LR achieved optimal performance (AUC = 1.0, κ = 0.98, F1 = 0.987), with SVM (AUC = 0.995) and XG-Boost (AUC = 0.982) also showing higher performance. SHAP and model breakdown analyses identified Apgar scores (at 1st and 5th minutes), birth weight, maternal health, and prenatal visit frequency as key predictors. Fairness assessments across socioeconomic subgroups indicated minimal bias (DIR > 0.8). The integration of XAI enhances transparency, supports clinician trust, and enables equitable decision-making. This framework bridges the gap between predictive accuracy and clinical usability, offering a scalable tool for early intervention. Policy recommendations include embedding this XAI-enhanced model into antenatal care systems to support evidence-based decisions and accelerate progress toward neonatal survival goals in resource-limited settings.
Abstract: Neonatal mortality remains a critical public health challenge in Kenya, with a rate of 21 per 1,000 live births—well above the SDG 3.2 target. While machine learning (ML) offers potential for risk prediction, most models lack transparency and clinical interpretability, limiting their adoption in low-resource settings. This study presents an explain...
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