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Machine Learning-Based Prediction of Unmet Need for Family Planning Among Reproductive-Age Married Women in Nigeria

Background: Unmet Need for Family Planning (UMFP) remains a public health concern in developing countries, particularly in Nigeria, where women who want to stop or delay childbearing are not using contraception. Although, previous studies identified factors influencing UMFP, limited evidence exists on how these factors are prioritised and used to predict the probability of UMFP. This study prioritised factors influencing UMFP and predicted its probability among married women of reproductive age in Nigeria.

Methodology: This cross-sectional study used 2003, 2008, 2013, 2018 and 2024 rounds of Nigerian Demographic and Health Survey (NDHS), which applied two-stage cluster sampling. Sub-samples extracted were 3,651 (2003), 17,316 (2008), 18,600 (2013), 19,318 (2018) and 16,111 (2024) currently married women of reproductive age. Logistic regression identified significant factors influencing UMFP,while dominance analysis (ΔR² ≥0.10) priortised their relative contributions. Decision trees, random forest, support vector machine and KNN models were trained on 2003-2018data and tested on 2024. Model performance was assessed using F1-score, precision, accuracy, recall and AUC. Decomposition analysis examined contributors to changes in UMFP. All analyses were conducted at α0.05.

Result: Standardised prevalence of UMFP increased from 29.3% (2003) to 36.0% (2024). Of 21 identified significant factors, dominant factors were Number of Surviving Children (NSC) with ΔR² ranging from 0.43-0.49, age (ΔR²: 0.33- 0.39), ideal number of children (ΔR²: 0.17-0.33), religion (ΔR²: 0.16-0.33), and wealth index (ΔR²: 0.10-0.19). Random Forest (AUC=76.2%; 95% CI=75.0-77.1). Highest predicted probability of UMFP was observed among women who live in the urban areas, belong to Yoruba ethnic group, in the rich wealth quintile, and practicing Christianity (p2003/2024=81.2%, p2008/2024=90.0%, p2013/2024=81.2%, and p2018/2024=97.2%).The rise in UMFP (rate=1.43) was mostly attributed to women’s NSC and religion.

Conclusion: Targeted, parity- sensitive and religiously responsive interventions are essential t reduce UMFP in Nigeria.