ArticlePLOS digital health2026
Modeling predictors of incomplete antenatal care utilization among reproductive-age women in Ethiopia using machine learning algorithms and SHAP interpretation.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The incidence of maternal deaths from preventable pregnancy-related conditions remains alarmingly high at 303,000 annually, with over 800 women dying daily from avoidable causes. Ethiopia is one of eight sub-Saharan African countries that are identified as global hot spots for maternal mortality. Thus, this study aimed to model predictors of incomplete ANC utilization among reproductive-aged women in Ethiopia using explainable machine learning algorithms. This study employed the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) dataset. Data preparation techniques such as feature engineering, data splitting, handling missing values, resolving imbalanced categories, and outlier removal were used to clean the data. Six popular machine learning classifiers were implemented in R 4.4.2 and Python 3.11.5 via Jupyter Notebook through the Scikit-learn and XGBoost packages and evaluated using multiple permanence matrices. Finally, Shapley Additive exPlanations (SHAP) analysis was used to clarify the impact of the most important predictors on the model's output. This study included 3979 women who had given birth during the five years prior to the survey out of the 8,885 interviewed women. Random forest (RF) was found to be the best model for modeling predictors of incomplete ANC utilization in Ethiopia, with 73% accuracy and 79% area under the ROC curve. Older age (25 and 34), residence area, being in the Benishangul-Gumuz, Tigray region, Harari region and wealth indices were top predictors of incomplete ANC utilization among reproductive-age women in Ethiopia. This study found that young women in rural areas, having low-income indices and low levels of education, as well as those living in the Somali and Harari regions, are more likely to experience incomplete ANC utilization. Policymakers and stakeholders should prioritize these vulnerable groups when designing policies and maternal health services to improve ANC utilization and reduce maternal mortality in Ethiopia.
Identifiers
What OpenQuestion holds
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.