Exploring Ensemble Learning Techniques for Infant Mortality Prediction: A Technical Analysis of XGBoost Stacking AdaBoost and Bagging Models.
Journal:
Birth defects research
PMID:
39917850
Abstract
BACKGROUND: Infant mortality remains a critical public health issue, reflecting the overall health and well-being of a population. Accurate prediction of infant mortality is crucial, as it enables healthcare providers to identify at-risk populations and implement targeted interventions. By analyzing factors such as maternal education, prenatal care access, nutrition, and environmental influences, predictions help in designing effective programs aimed at reducing infant deaths.