Predicting the risk of metabolic-associated fatty liver disease in the elderly population in China: Construction and evaluation of interpretable machine learning models

Yingxin Zeng^1*Chaobing Yang²Xin Yang¹Xinmei Zhang¹Guodong Xia^3*

• ¹The Affiliated Hospital of Southwest Medical University, Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
• ²Department of Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, The Affiliated Hospital of Southwest Medical University, Luzhou, China
• ³Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University; Health Management Center, The Affiliated Hospital of Southwest Medical University, Luzhou, China

With the rising incidence of metabolic dysfunction-associated fatty liver disease (MAFLD) in the elderly population, this study aimed to develop an optimal screening model by comparing ten different machine learning (ML) algorithms to identify high-risk elderly individuals using routine health examination data. The study included 2,635 individuals aged 60 years and older who underwent annual health examinations at the Health Management Center of Southwest Medical University Affiliated Hospital from January to December 2024. Initial feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression, followed by univariate and multivariate logistic regression analysis to identify nine independent predictive factors. Predictive models were constructed using ten ML algorithms, and model performance was evaluated based on discriminative ability, calibration ability, and clinical utility. Feature importance was visualized and individual-level interpretability was provided using the SHapley Additive exPlanations (SHAP) method. The final analysis included nine variables. After 10-fold cross-validation and hyperparameter tuning, the Random Forest (RF) model performed best, achieving an area under the curve (AUC) of 0.892 (95% CI: 0.870–0.914) in the validation cohort. Feature importance analysis revealed that the TyG-BMI index, height, and albumin levels played significant roles in predicting MAFLD risk. Machine learning models, particularly the random forest algorithm, can effectively predict the risk of MAFLD in the elderly population. These models may assist clinicians in early screening and intervention, thereby improving patient outcomes.