Using machine learning to identify the most important predictors of fatty liver index in healthy young Taiwanese men

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. While many factors have been associated with NAFLD, their relative importance in healthy young populations remains unclear. In this study, we enrolled 7,037 healthy young Taiwanese men aged 20-50 years and applied five machine learning (Mach-L) methods to identify the most important predictors of the fatty liver index (FLI). Two models were constructed: Model 1 included all 28 variables, while Model 2 excluded body fat (BF) to unmask the contribution of other factors. In Model 1, BF was the most important predictor (100% relative importance), followed by serum glutamic pyruvic transaminase (SGPT; 34.54%), high-density lipoprotein cholesterol (HDL-C; 16.51%), age (9.91%), uric acid (UA; 7.17%), and fasting plasma glucose (FPG; 6.25%). In Model 2, after removing BF, the most important predictors were SGPT (100%), HDL-C (36.82%), UA (24.90%), C-reactive protein (21.23%), age (10.05%), and FPG (8.17%). All five machine learning methods outperformed traditional multiple linear regression. These findings highlight the central role of adiposity while also revealing the independent contributions of metabolic, inflammatory, and hepatic factors to FLI in young healthy men.