Automated Machine Learning Model for Non-Alcoholic Fatty Liver Disease Prediction and External Cohort Validation

Non-alcoholic fatty liver disease (NAFLD) is a common liver disorder associated with obesity, insulin resistance, and metabolic syndrome, often going undiagnosed until advanced stages. Traditional diagnostic methods, including imaging and liver biopsy, have limitations in early detection. There is a need for an efficient, non-invasive tool for early NAFLD screening. Using automated machine learning (AutoML) technology, a diagnostic model for NAFLD was developed leveraging a large dataset from NHANES (n = 2677). Additionally, an independent external validation cohort (n = 200) was employed to assess the external validity and performance of the model. For selection of promising clinical features, A two-stage feature selection method was applied, combining LASSO regression with ChatGPT-4-based intelligent analysis. Subsequently, the AutoML process, which integrated multiple machine learning algorithms, was performed for model training and validation. The model's performance was evaluated using ROC curves, F1 scores, and SHapley additive explanation (SHAP) analysis. The GBM model achieved an AUC of 0.843 in the training set, 0.851 in the testing set, and 0.945 in the external validation set, demonstrating high diagnostic accuracy across different datasets. Key predictors, including BMI, triglycerides, and GGT, were identified as significant contributors to the model's predictions. SHAP analysis further confirmed the importance of these variables in predicting NAFLD. The AutoML-driven diagnostic model for NAFLD demonstrated significantly improved early-detection performance, offering a reliable, non-invasive, and efficient alternative to conventional diagnostic methods. This method holds great potential for broader clinical application in NAFLD, diminishing dependence on expert knowledge while improving diagnostic precision.