Predicting abnormal birth weight and identifying associated factors using machine learning in the Hararghe Health and Demographic Surveillance System, Ethiopia

BACKGROUND: Birth weight is a reliable indicator of intrauterine growth and an important predictor of neonatal survival, growth, and long-term development. Globally, approximately 15.5% of live births are low birth weight, and nearly 10% are macrosomic (high birth weight), with a substantial proportion of these cases occurring in sub-Saharan Africa. Birth weights outside the normal range of 2,500-4,000 g are considered abnormal and are associated with increased risks of neonatal and maternal complications. Ethiopia is similarly affected by the growing burden of abnormal birth weight. METHODS: A retrospective cross-sectional study design using secondary HDSS data collected from 2015 to 2022 was employed. This design was considered appropriate because the study aimed to develop machine learning models using routinely collected surveillance data to predict abnormal birth weight and identify associated factors, rather than to establish causal relationships. All singleton births were included, and those with missing birth weight data were excluded. Six machine learning algorithms identified from the literature were built and compared to identify the best-performing model for predicting abnormal birth weight. Prior observational studies and expert opinion were used to select the candidate features for all models. The synthetic minority oversampling technique (SMOTE) was used to manage the imbalance in the dataset. The dataset was split into training (80%, n = 9,242) and testing (20%, n = 2,311) subsets for model development and evaluation. Hyper-parametric tuning was performed using grid search combined with 10-fold cross-validation to optimize model performance and reduce over-fitting. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, precision, F1-score, and Kappa. Feature importance analysis was done using Shapley Additive explanation (SHAP) values. RESULTS: The Descriptive analysis of 11,553 singleton births showed that 10.78% of the newborns had high birth weight (HBW) and 9.28% had low birth weight (LBW). The eXtreme Gradient Boosting (XGBoost) model performed best by achieving an AUC of 0.835, an accuracy of 0.72, a precision of 0.67, an F1-score of 0.63, a recall of 0.54, and a kappa of 0.52 for abnormal birth weight prediction. The feature importance analysis showed that the top predictors for the low birth weight (LBW) include maternal educational status, age at first delivery, and antenatal care (ANC) visit, while high birth weight (HBW) was strongly predicted by antenatal care (ANC) visit, maternal literacy status, age at first delivery, and maternal education. CONCLUSION: Machine learning models showed moderate performance in predicting abnormal birth weight using HDSS surveillance data. Maternal educational characteristics, age at first delivery, and ANC utilization were identified as important predictive features. However, the findings should be interpreted cautiously because the model identified predictive associations rather than causal relationships. Further studies incorporating additional maternal clinical and nutritional variables, as well as external validation datasets, are recommended to improve predictive performance and generalizability.