Predicting Risky Behavior Among Pregnant Women in Eastern African countries using Pooled Demographic and Health Survey (DHS) Data: An Explainable Machine Learning Approach
4 affiliations
- 1Department of Health Informatics, College of Medicine and Health Science, Wollo University, P.O. Box 1145, Dessie, Ethiopia
- 2Department of Oncology, Addis Ababa University College of Health Science, Tikur Anbessa specialized hospital, Addis Ababa University, Addis Ababa, Ethiopia
- 3Department of Biomedical Science, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia
- 4Department of Occupational and Safety, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia
Abstract
Introduction: Consumption of unhealthy substances such as tobacco use, and engagement in unsafe sexual activity are the major contributors to morbidity and mortality for pregnant women and the fetus. Although, risky behavior among pregnant women was complex and multifactorial public health concern, there is a limited study on those combined risk behaviors and their predictors among pregnant women in Eastern African countries using machine learning. Therefore, this study was conducted to examine the predictors of risky behaviors among pregnant women in Eastern African countries by using machine learning approach.
Method: The study used a cross-sectional study design from Demograpic and Health Survey data collected from 2012 - 2022 in Eastern African countries. STATA 17 was used for data extraction and preparation. Anaconda 3 and R softwares were used for data preprocessing, and analysis. Model performance was evaluated using accuracy, sensitivity, precision, F1-score and Area Under the Curve (AUC). Finally, the Shapley Additive Explanation (SHAP) was applied to further explain the predictors of risky behaviors among pregnant women.
Results: The pooled prevalence of risky behavior among pregnant women in Eastern African countries were 16.38% (95% CI: 12.63, 20.82). The Light Gradient Boosting Machine (LGBM) achieved an accuracy of 90% and AUC score of 0.96. The analysis revealed that pregnant women who lived in rural areas, being from poor and middle wealth income, women whose husbands had primary education, and women who do not exposed to media were among the most important predictors of risky behavior. Whereas women who are employed, women who utilized more than three Ante natal care service, and women aged between 25 and36 were contributed to a lower predicted likelihood of risky behaviors.
Conclusion: Risky behavior was a complex multifactorial public health challenge in Eastern African countries. The Light Gradient Boosting Machine (LGBM) was the best-performing model for predicting risky behaviors among pregnant women in Eastern African countries. The identified predictors provide valuable insights for developing targeted public health interventions to enhancing and increasing women’s access to more than three Ante natal care visits, enhancing husbands’ educational attainment, extending and implementing targeted media use, and empowering women economically. Expanding media campaigns and community outreach program to promote early and progressive adequate Ante natal care, particularly in rural areas.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12884-026-09725-7.
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BibTeX
@article{jemil2026predicting,
title = {{Predicting Risky Behavior Among Pregnant Women in Eastern African countries using Pooled Demographic and Health Survey (DHS) Data: An Explainable Machine Learning Approach}},
author = {Halid Worku Jemil and Sonia Worku Semayneh and Altaseb Beyene Kassaw and Anmut Endalkachew Bezie},
journal = {BMC Pregnancy and Childbirth},
year = {2026},
month = aug,
doi = {10.1186/s12884-026-09725-7},
url = {https://doi.org/10.1186/s12884-026-09725-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predicting Risky Behavior Among Pregnant Women in Eastern African countries using Pooled Demographic and Health Survey (DHS) Data: An Explainable Machine Learning Approach
AU - Halid Worku Jemil
AU - Sonia Worku Semayneh
AU - Altaseb Beyene Kassaw
AU - Anmut Endalkachew Bezie
PY - 2026
DA - 2026/08/10
JO - BMC Pregnancy and Childbirth
DO - 10.1186/s12884-026-09725-7
UR - https://doi.org/10.1186/s12884-026-09725-7
AB - Introduction: Consumption of unhealthy substances such as tobacco use, and engagement in unsafe sexual activity are the major contributors to morbidity and mortality for pregnant women and the fetus. Although, risky behavior among pregnant women was complex and multifactorial public health concern, there is a limited study on those combined risk behaviors and their predictors among pregnant women in Eastern African countries using machine learning. Therefore, this study was conducted to examine the predictors of risky behaviors among pregnant women in Eastern African countries by using machine learning approach. Method: The study used a cross-sectional study design from Demograpic and Health Survey data collected from 2012 - 2022 in Eastern African countries. STATA 17 was used for data extraction and preparation. Anaconda 3 and R softwares were used for data preprocessing, and analysis. Model performance was evaluated using accuracy, sensitivity, precision, F1-score and Area Under the Curve (AUC). Finally, the Shapley Additive Explanation (SHAP) was applied to further explain the predictors of risky behaviors among pregnant women. Results: The pooled prevalence of risky behavior among pregnant women in Eastern African countries were 16.38% (95% CI: 12.63, 20.82). The Light Gradient Boosting Machine (LGBM) achieved an accuracy of 90% and AUC score of 0.96. The analysis revealed that pregnant women who lived in rural areas, being from poor and middle wealth income, women whose husbands had primary education, and women who do not exposed to media were among the most important predictors of risky behavior. Whereas women who are employed, women who utilized more than three Ante natal care service, and women aged between 25 and36 were contributed to a lower predicted likelihood of risky behaviors. Conclusion: Risky behavior was a complex multifactorial public health challenge in Eastern African countries. The Light Gradient Boosting Machine (LGBM) was the best-performing model for predicting risky behaviors among pregnant women in Eastern African countries. The identified predictors provide valuable insights for developing targeted public health interventions to enhancing and increasing women’s access to more than three Ante natal care visits, enhancing husbands’ educational attainment, extending and implementing targeted media use, and empowering women economically. Expanding media campaigns and community outreach program to promote early and progressive adequate Ante natal care, particularly in rural areas. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12884-026-09725-7.
SN - 1471-2393
ER -