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

Mingjie Li; Yadong Li; Lihui Chen; Han Chen; Lishengdan Chen; Wenyue Liu; Gang Xu; Shuying Chen

doi:10.3791/70033

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Research Article

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

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DOI:

10.3791/70033

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July 3rd, 2026

Mingjie Li^*¹ , Yadong Li^*² , Lihui Chen^*³ , Han Chen⁴ , Lishengdan Chen⁵ , Wenyue Liu⁶ , Gang Xu⁴ , Shuying Chen⁴

¹Department of Laboratory Medicine, Fujian Medical University Union Hospital, Fujian Medical University, ²Department of Clinical Laboratory. Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, ³Department of Laboratory Medicine, Fujian Pingtan Comprehensive Experimental Area Hospital, ⁴Department of Clinical Laboratory, Key Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, ⁵Department of Gastroenterology, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, ⁶Department of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University

^* These authors contributed equally

Summary

A step-by-step AutoML protocol is presented to screen for nonalcoholic fatty liver disease using NHANES data and an external cohort. The method automates feature prioritization and model selection (GBM), and includes SHAP-based interpretation and external validation for reproducible clinical deployment.

Abstract

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.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is one of the most prevalent liver diseases globally, currently affecting approximately 35% of the adult population worldwide, and is not attributable to excessive alcohol consumption¹. It is characterized by the accumulation of fat in more than 5% of hepatocytes, which can potentially progress to more severe conditions, including inflammation, liver fibrosis, cirrhosis, and ultimately liver failure²^,³. Currently, the traditional methods for diagnosing NAFLD primarily rely on abnormal liver biochemical markers and ultrasound ima....