preprint · medRxiv
This study developed and validated interpretable machine learning models to predict low birth weight in Ethiopia, a significant contributor to neonatal and infant mortality. Using data from the Ethiopian Demographic and Health Survey, the research focused on features accessible during early antenatal visits, including sociodemographic factors. Six machine learning algorithms were evaluated, with XGBoost demonstrating superior predictive performance (AUC-ROC 0.947). The models identified key risk factors such as maternal anemia, short inter-pregnancy intervals, low maternal BMI, rural residence, low household wealth, and delayed or absent first-trimester antenatal care. The findings suggest that these interpretable models can accurately stratify low birth weight risk early in pregnancy.
Low birth weight is a leading cause of infant mortality. This research offers a way to identify at-risk pregnancies early using readily available information. This allows healthcare providers to implement timely, targeted interventions, potentially saving lives and improving child health outcomes, especially in settings with limited resources.
This research provides a foundation for developing early-stage decision support tools for primary healthcare. These tools could enable healthcare workers to identify pregnant individuals at high risk of delivering low birth weight babies, facilitating targeted interventions. The models, based on early pregnancy and sociodemographic features, are suitable for integration into digital health platforms in resource-limited settings.
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Abstract Background Low birth weight remains a primary driver of neonatal and infant mortality in Ethiopia. Machine learning models can assist early risk identification, yet clinical adoption is often limited by black box algorithms and late pregnancy predictor variables. This study aimed to develop and validate interpretable machine learning models using early pregnancy and sociodemographic features from a national survey dataset. Methods Secondary data from the nationwide Ethiopian Demographic and Health Survey were analyzed. Predictors were restricted to features accessible during early antenatal visits. Six machine learning algorithms were trained and evaluated on an independent holdout test set: Logistic Regression, Decision Tree, Support Vector Machine, Gradient Boosting, Random Forest and Extreme Gradient Boosting (XGBoost). Imbalance was addressed using synthetic oversampling on the training set. Model explainability was established through Shapley Additive exPlanations (SHAP). Results Out of 12876 births, 4249 (33%) were categorized as low birth weight / small birth size. XGBoost achieved superior predictive performance with an AUC-ROC of 0.947 (95% CI: 0.910-0.938) on the test set, outperforming lasso ML (0.8637) and standard logistic regression (0.8088). Key global predictive drivers identified by SHAP values included maternal anemia status, short inter pregnancy interval (< 18 months), low maternal BMI (< 18.5 kg/m^2), rural residence, lowest household wealth quintile and delayed or non-attendance of first trimester antenatal care. Conclusion Machine learning models trained on early pregnancy and demographic features can accurately predict low birth weight risk in Ethiopia. Integrating interpretable frameworks into primary healthcare decision support tools provides a viable strategy for early risk stratification and targeted interventions in resource-limited settings.
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DOI: 10.64898/2026.08.17.26360212
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