ORIGINAL RESEARCH article
Front. Glob. Women’s Health
Sec. Maternal Health
Volume 6 - 2025 | doi: 10.3389/fgwh.2025.1488391
Predicting Delayed Antenatal Care Initiation among Pregnant Women in East Africa: Using Machine Learning Algorithms
Provisionally accepted
Nebebe Demis Baykemagn*
Eliyas Taye Addisu Taye
Mequannent SharewTirualem Zeleke YehualaMakda Fekadie TewelgneFetlework Gubena ArageAdem Tsegaw Zegeye- University of Gondar, Gondar, Ethiopia
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Sub-Saharan Africa has the highest rate of maternal death due to pregnancy-related complications. The delayed onset of Antenatal care (ANC) is a major underlying factor for maternal mortality. The overall well-being and health of pregnant women and their fetuses greatly depend on the timely initiation of ANC care. This study aims to identify the top predictors of delayed antenatal care initiation in East Africa, including Ethiopia, to provide input for policymakers. Methodology We employed secondary data from the Demographic Health Surveys conducted across ten East African countries between 2016 and 2023. After preprocessing the data, which included cleaning and feature selection through Recursive Feature Elimination, we addressed class imbalance using Synthetic Minority Over-sampling Technique (SMOTE). We then applied seven supervised learning algorithms to develop a robust machine learning model. The LGBM Classifier emerged as the top performer for predicting delayed antenatal care initiation, achieving accuracy of 75%, an AUC score of 81%, an F1 score of 78%, a recall of 79%, and a precision of 77%. Additionally, we employed association rule mining to further analyze. Result: Home delivery, low household income, a large number of household members, unemployment, being a younger woman, not using family planning methods, low educational level, and rural residence are predictors of delayed antenatal care initiation. Conclusion: The prevalence of late antenatal care (ANC) initiation is high (65%). Promote family planning utilization, targeted health literacy campaigns, youth-friendly programs, expand mobile clinics, and promote skilled birth attendance to increase timely ANC initiation are recommended
Keywords: ANC, machine learning, artificial intelligence, East Africa, Pregnancy
Received: 30 Aug 2024; Accepted: 26 Aug 2025.
Copyright: © 2025 Baykemagn, Taye, Sharew, Yehuala, Tewelgne, Arage and Zegeye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nebebe Demis Baykemagn, University of Gondar, Gondar, Ethiopia
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