Prediction of Metabolic Dysfunction–Associated Steatotic Liver Disease via Advanced Machine Learning Among Chinese Han Population

作者：Na Wu, Mofan Feng, Hanhua Zhao, Shuang Wei, Xinyu Shi, Xinying Xiong, Wenjun Zhou, Shengfu You, Hualing Song, Han Young Yu, Jianyang Wang, Lei Zhang, Guang Ji, Baocheng Liu · 发表于：Obesity Surgery · 年份：2025 · DOI：10.1007/s11695-025-08096-w · 被引用次数：6 · 研究领域：Liver Disease Diagnosis and Treatment、Hepatitis C virus research、Liver Disease and Transplantation

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 ...