ArticlePLOS digital health2023
Machine learning modeling for identifying predictors of unmet need for family planning among married/in-union women in Ethiopia: Evidence from performance monitoring and accountability (PMA) survey 2019 dataset.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 13 citations in OpenAlex.
- Modeling predictors of incomplete antenatal care utilization among reproductive-age women in Ethiopia using machine learning algorithms and SHAP interpretation.PLOS digital health · 2026Article
- Supervised machine learning-based prediction of modern contraceptive use among sexually active women in Nepal.PLOS digital health · 2026Article
- Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.Scientific reports · 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
- Classification of knowledge of fertility period among adolescent girls in East Africa from 2012 to 2022: Machine learning algorithm.PLOS digital health · 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
- Identifying determinants of unmet healthcare needs in South Korea: a comparison of machine learning approaches for precision public health.BMC public health · 2025Article
- Article
- Unmet need for contraception? Understanding postpartum family planning desires and use in Kumasi, Ghana.Frontiers in reproductive health · 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
- Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques.Scientific reports · 2024Article
- Prediction of delayed breastfeeding initiation among mothers having children less than 2 months of age in East Africa: application of machine learning algorithms.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unmet need for contraceptives is a public health issue globally that affects maternal and child health. Reducing unmet need reduces the risk of abortion or childbearing by preventing unintended pregnancy. The unmet need for family planning is a frequently used indicator for monitoring family planning programs. This study aimed to identify predictors of unmet need for family planning using advanced machine learning modeling on recent PMA 2019 survey data. The study was conducted using secondary data from PMA Ethiopia 2019 cross-sectional household and female survey which was carried out from September 2019 to December 2019. Eight machine learning classifiers were employed on a total weighted sample of 5819 women and evaluated using performance metrics to predict and identify important predictors of unmet need of family planning with Python 3.10 version software. Data preparation techniques such as removing outliers, handling missing values, handling unbalanced categories, feature engineering, and data splitting were applied to smooth the data for further analysis. Finally, Shapley Additive exPlanations (SHAP) analysis was used to identify the top predictors of unmet need and explain the contribution of the predictors on the model's output. Random Forest was the best predictive model with a performance of 85% accuracy and 0.93 area under the curve on balanced training data through tenfold cross-validation. The SHAP analysis based on random forest model revealed that husband/partner disapproval to use family planning, number of household members, women education being primary, being from Amhara region, and previously delivered in health facility were the top important predictors of unmet need for family planning in Ethiopia. Findings from this study suggest various sociocultural and economic factors might be considered while implementing health policies intended to decrease unmet needs for family planning in Ethiopia. In particular, the husband's/partner's involvement in family planning sessions should be emphasized as it has a significant impact on women's demand for contraceptives.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.