AJOG global reports · 2026
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.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tilahun Befkad Derese, Ayele Mulat, Lake Eyob Shitie, Yilak Gizachew, Kitaw Tegene Atamenta, Attinafu Bekalu Alemayehu
Аннотация
BACKGROUND: Long-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.
OBJECTIVE: This study aimed to predict LARC utilization and identify key determinants among women of reproductive age in SSA using advanced machine learning techniques.
METHODS: A 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.
RESULTS: The 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).
CONCLUSION: Machine 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.
OBJECTIVE: This study aimed to predict LARC utilization and identify key determinants among women of reproductive age in SSA using advanced machine learning techniques.
METHODS: A 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.
RESULTS: The 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).
CONCLUSION: Machine 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.