  Sepsis requires urgent diagnosis, but research is predominantly focused on
Western datasets. In this study, we perform a comparative analysis of two
ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD
dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis
reveals the effectiveness of these methods in addressing healthcare data
imbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight
edge in computational efficiency and scalability. The study paves the way for
the broader application of machine learning in critical care, thereby expanding
the reach of predictive analytics in healthcare globally.
