Researchers Xiaolong Li and team used interpretable machine learning techniques, including LASSO and XGBoost, to assess pre-diabetes risk from the CHNS dataset. By evaluating factors like age, BMI, and cholesterol, their model presents a reliable strategy for early detection and timely intervention against diabetes.