Exploring Ensemble Learning Techniques for Infant Mortality Prediction: A Technical Analysis of <scp>XGBoost</scp> Stacking <scp>AdaBoost</scp> and Bagging Models

The simulation results revealed that ensemble learning models are highly effective in predicting infant mortality rates in India, with significant regional disparities observed. For example, the Northeast region exhibited the highest predicted infant mortality rates, while the South region recorded the lowest. These findings underscore the need for targeted interventions in high-mortality areas to reduce disparities. The study highlights the efficacy of ensemble learning models, particularly XGBoost, in predicting infant mortality in India. The findings emphasize the critical role of improving maternal education, access to prenatal care, and reducing socioeconomic disparities.