Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: Data from the Araraquara Cohort Study
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Background
Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost, Catboost, and LightGBM.
Methods
Machine learning algorithms (Random Forest, XGBoost, Catboost, and LightGBM) were trained and evaluated using cross-validation and the SMOTE technique to correct class imbalance. Model performance was measured using the AUROC metric, and variable importance was analyzed with Shapley values to ensure model interpretability.
Results
The XGBoost model achieved the best performance with an AUROC of 0.94. Catboost and Random Forest also showed excellent results, confirming the effectiveness of these models in predicting LBW.
Conclusion
Machine learning, combined with SMOTE, proved to be an effective approach for predicting LBW. XGBoost stood out as the most accurate model, but Catboost and Random Forest also provided solid results. These models can be applied to identify high-risk pregnancies, improving perinatal outcomes through early interventions.
