ArticleBMC medical informatics and decision making2023
Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 34 citations in OpenAlex.
- 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
- Modeling predictors of incomplete antenatal care utilization among reproductive-age women in Ethiopia using machine learning algorithms and SHAP interpretation.PLOS digital health · 2026Article
- Predicting facility delivery and its determinants among reproductive-age women in Ethiopia using machine learning algorithm: evidence from performance monitoring for action Ethiopia 2019 dataset.BMC pregnancy and childbirth · 2026Article
- Application of machine learning algorithms for prediction of abortion and its determinants among women of reproductive-age in East African countries.Scientific reports · 2026Article
- [Unmet need for contraception in the racialized population in Colombia: analysis of the results of the 2015 National Demographic and Health Survey].Cadernos de saude publica · 2026Article
- Leveraging machine learning algorithm to predict minimum dietary diversity among children aged 6-23 months in Ethiopia.PLOS global public health · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Modern Contraceptive Use Among Young Women in Kampala Slums: Research Findings from the TOPOWA Study.International journal of environmental research and public health · 2025Article
- Anti-epileptic medication induced disturbed calcium-vitamin D metabolism: A behavioral analysis using association rule mining technique.World journal of experimental medicine · 2025Article
- Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025.BMC medical informatics and decision making · 2025Article
- Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016-2019.BMC pregnancy and childbirth · 2025Article
- Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa.Frontiers in global women's health · 2025Article
- Predictors of community-based health insurance enrollment among reproductive-age women in Ethiopia based on the EDHS 2019 dataset: a study using SHAP analysis technique, 2024.Frontiers in public health · 2025Article
- Article
- Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.PloS one · 2025Article
- Predicting delayed antenatal care initiation among pregnant women in East Africa: using machine learning algorithms.Frontiers in global women's health · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Article
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlobally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces contraceptive coverage, family planning program effectiveness and contributes to undesired fertility. Hence understanding potential predictors of contraceptive discontinuation is crucial to reducing its undesired outcomes. Predicting the risk of discontinuing contraceptives is also used as an early-warning system to notify family planning programs. Thus, this study could enable to predict and determine the predictors for contraceptive discontinuation in Ethiopia. METHODOLOGY: Secondary data analysis was done on the 2016 Ethiopian Demographic and Health Survey. Eight machine learning algorithms were employed on a total sample of 5885 women and evaluated using performance metrics to predict and identify important predictors of discontinuation through python software. Feature importance method was used to select top predictors of contraceptive discontinuation. Finally, association rule mining was applied to discover the relationship between contraceptive discontinuation and its top predictors by using R statistical software.
resultRandom forest was the best predictive model with 68% accuracy which identified the top predictors of contraceptive discontinuation. Association rule mining identified women's age, women's education level, family size, husband's desire for children, husband's education level, and women's fertility preference as predictors most frequently associated with contraceptive discontinuation.
conclusionResults have shown that machine learning algorithms can accurately predict the discontinuation status of contraceptives, making them potentially valuable as decision-support tools for the relevant stakeholders. Through association rule mining analysis of a large dataset, our findings also revealed previously unknown patterns and relationships between contraceptive discontinuation and numerous predictors.
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