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.
Adem et al. (Thu,) studied this question.