Aims & Background: Currently, most predictions of metabolic associated fatty liver disease (MAFLD) in school-aged children utilize indicators that usually predict nonalcoholic fatty liver disease (NAFLD). The present study aimed to develop new predictive models and predictors for children with MAFLD, which could enhance the feasibility of MAFLD screening programs in the future. Methods A total of 331 school-aged obese/overweight children were recruited from six primary schools in Ningbo city, China. Hepatic steatosis and fibrosis were detected with controlled attenuation parameter (CAP) and liver stiffness measurement (LSM), respectively. Machine learning methods to build a set of variables to predict MAFLD in children. Then, the areas under the curve (AUC) of multiple models were compared to predict pediatric MAFLD. Results Compared with non-MAFLD children, children with MAFLD had more obvious metabolic abnormalities as they had higher anthropometric indicators, alanine aminotransferase, fasting plasma glucose, and higher inflammation indicators (white blood cell count, hemoglobin, neutrophil) (all P
