Abstract
BACKGROUND: Metabolic-associated fatty liver disease (MAFLD) is the most common cause of chronic liver disease and remains under-recognized within the health check-up population. Ultrasonography during physical examination fail to accurately identify at-risk patients as they involve multiple metabolic aspects. AIM: To rapidly identify hepatic steatosis patients from high-metabolic-risk populations and reduce medical costs. METHODS: We analyzed all data from a prospective cohort study to identify potential predictors of MAFLD risk. The LASSO and recursive feature elimination were used to screen for feature selection. Four machine learning models were employed to construct the prediction model for hepatic steatosis. RESULTS: We found that 86.2% of the 1011 individuals in the trial phase exhibited metabolic abnormalities, with 70.8% presenting with hepatic steatosis. After data cleaning, 711 participants (207 non-MAFLD patients vs 504 MAFLD patients) were included, and the prediction models were validated. After overlapping and reducing the feature set based on feature importance ranking, we developed an interpretable final XGBoost model with 10 features, achieving an area under the curve of 0.82. CONCLUSION: We have introduced a valuable noninvasive tool for efficiently identifying hepatic steatosis patients in high-metabolic-risk populations. This tool may improve screening effectiveness and reduce medical costs.