Abstract
BACKGROUND: Early and accurate diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) is crucial for implementing effective treatment and management strategies, as the disease can progress to more severe conditions such as cirrhosis and liver cancer. This study aimed to evaluate the performance of machine learning (ML) methods in detecting MASLD and provide a more effective and efficient diagnostic approach. METHODS: Data were collected from outpatient participants undergoing annual health checks at the Pudong District Health Care Service Centers in Shanghai, China. The discovery and independent validation cohorts included 8949 and 5973 participants, respectively. Initially, 47 variables were analyzed, and an ML-driven feature selection method identified 20 variables for MASLD prediction. To enhance clinical utility, a simplified panel of 7 variables was further derived: body mass index, albumin, alanine transaminase, glucose, high-density lipoprotein, triglyceride, and creatinine. Four ML models-k-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), and artificial neural networks (ANN)-were trained using both the 7-variable and full-variable datasets. RESULTS: In the independent test set, the 7-variable models demonstrated superior performance compared to the full-variable models. The AUC values for KNN, SVM, and ANN using the 7-variable set were 0.833, 0.753, and 0.848, respectively, significantly higher than those of the full-variable models (KNN, 0.683; SVM, 0.705; ANN, 0.847). The robustness of the 7-variable panel was further validated by its generalizability across the independent cohort. CONCLUSIONS: This study establishes a streamlined ML-driven diagnostic framework for MASLD, leveraging routinely measured clinical variables to achieve high accuracy. The simplified 7-variable model enhances early detection and risk stratification while reducing diagnostic complexity and cost. By enabling proactive interventions through actionable biomarkers (e.g., glucose and lipid profiles), this approach holds significant potential for large-scale population screening and prevention of MASLD-related complications. The findings underscore the transformative role of ML in optimizing chronic liver disease management and advancing precision hepatology.