ArticleBMC health services research2024
Exploring machine learning algorithms to predict not using modern family planning methods among reproductive age women in East Africa.
Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- 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.AJOG global reports · 2026Article
- Determinants of modern family planning use among married adolescents in Bangladesh: evidence from a machine learning approach.BMC public health · 2026Article
- Supervised machine learning-based prediction of modern contraceptive use among sexually active women in Nepal.PLOS digital health · 2026Article
- Classification of knowledge of fertility period among adolescent girls in East Africa from 2012 to 2022: Machine learning algorithm.PLOS digital health · 2026Article
- Insights into long-acting reversible contraceptive practices in Sub-Saharan Africa: A machine learning perspective.PloS one · 2026Article
- Machine learning-based prediction of household sanitation facility access in Sub-Saharan Africa: insights from DHS data (2012-2024).Archives of public health = Archives belges de sante publique · 2025Article
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Abstract
backgroundThe use of the modern family planning method provides chances for women to reach optimal child spacing, increase quality of life, increase economic status, achieve the desired family size, and prevent unsafe abortions and maternal and perinatal deaths. However, use modern family planning is low among reproductive age women in East Africa. Therefore, this study aimed at predicting not using modern family planning and identifying its associated factors among reproductive age women in east Africa using machine learning methods.
methodsThe data set was obtained from a demographic health survey in East Africa. This study used data on weighted sample of 92,564 women who were in their reproductive age. Python software was utilized for data processing, and machine learning models such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), SVM (Support Victor Machine), and K-Nearest Neighbor Decision Tree (DT) were implemented. In this work, we evaluated the predictive models' performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve.
resultIn this study, 92,564 reproductive women's was used in the final analysis. Among the proposed machine learning models, XGB performed best overall in the proposed classifier, with an accuracy of 98.7%, precision of 99.8%, recall of 98%, 99.9% AUC score, had high specificity 98%, sensitivity 99.8%, and low error rate discovery was 0.013%. The most significant determinants of not using modern family planning in East Africa: lack of education, age 25-29, living in a rural area, being single, not knowing how to use a contraceptive, and smoking cigarette.
conclusionExtreme Gradient Boosting proved effective in predicting not using modern family planning, offering valuable insights for interventions and policy decisions in East Africa. Machine learning could help achieve early prediction and intervention on reproductive women's not using modern family planning. This leads to a recommendation for policy direction to reduce child and maternal mortality and improve living standards in east Africa.
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