Imbalanced Learning for Low-Birthweight Risk Prediction Across Multi-Institutional Maternal Records
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Abstract
Low birthweight remains a critical determinant of neonatal morbidity and mortality worldwide, presenting significant public health challenges and imposing substantial economic burdens on healthcare systems. Early and accurate prediction of low-birthweight risk during antenatal care could enable targeted interventions that mitigate adverse perinatal outcomes. However, the development of robust predictive models is severely hindered by the inherent class imbalance present in maternal electronic health records, where pathological low-birthweight cases constitute a small minority compared to healthy deliveries. Furthermore, models trained on single-institution datasets frequently suffer from poor generalization due to demographic and clinical practice variations. This paper presents a comprehensive investigation into imbalanced learning techniques applied to multi-institutional maternal data. We propose an integrated framework that combines advanced synthetic data generation strategies with cost-sensitive ensemble learning to address the extreme class disparity. By harmonizing disparate electronic health records from multiple independent healthcare networks, we evaluate the comparative efficacy of various class-balancing algorithms in predicting low birthweight. Our extensive analysis demonstrates that hybrid approaches, which simultaneously address data-level imbalance and algorithm-level bias, significantly enhance the detection of minority class instances without severely compromising overall diagnostic precision. The findings underscore the necessity of multi-institutional data integration and specialized imbalanced learning methodologies in developing generalizable, equitable clinical decision support systems for maternal and fetal medicine.
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