Nature

Data augmentation alters feature importance in XGBoost for CVD prediction

Machine learning models are powerful tools for cardiovascular disease (CVD) prediction, but their performance is often limited by dataset size and class imbalance. While data augmentation techniques ...
Nature

‌Application of XGBoost and logistic regression in predicting 90 days mortality for elderly severe acute renal failure patients

Acute renal failure (ARF) is one of the most common conditions encountered in the intensive care unit (ICU). ARF has a complex pathogenesis and due to the progressive weakening of the structure and ...