Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES

Abstract

XGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.