ArticleAJOG global reports2026
Determinants and predictive modeling of long-acting reversible contraceptive use in Sub-Saharan Africa: evidence from DHS data using machine learning and association rule mining.
Article in AJOG global reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLong-acting reversible contraceptives (LARCs), including intrauterine devices and implants, are highly effective in preventing unintended pregnancies. Despite their benefits, utilization remains low across many Sub-Saharan African (SSA) countries.
objectiveThis study aimed to predict LARC utilization and identify key determinants among women of reproductive age in SSA using advanced machine learning techniques.
methodsA secondary analysis was conducted using the latest Demographic and Health Survey (DHS) datasets from eight SSA countries, yielding a weighted sample of 29,016 women aged 15-49 years. Data preprocessing included cleaning, feature engineering, variable selection, and class balancing with SMOTE. Twelve machine learning models were developed, and the best-performing model was optimized using Bayesian methods. Association rule mining (Apriori algorithm) was applied to uncover hidden patterns among predictors.
resultsThe pooled prevalence of LARC use was 29% (95% CI: 21%-38%), with high between-country heterogeneity (I²=99.67%). Random Forest achieved the best performance after optimization, with an accuracy of 87.1%, AUC of 81.0%, and F1 score of 85.0%. Major predictors included country, education, parity, marital status, and age. Association rule mining showed that rural, uneducated, poor, and married women in Senegal and Burkina Faso had a higher likelihood of LARC use (Lift =2.25).
conclusionMachine learning identifies potential predictors of LARC utilization and identifies key determinants in SSA. Targeted interventions focusing on rural, low-income, and low-education groups may improve LARC uptake and reduce unmet family planning needs.
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