Academic Journal
Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.
| Title: | Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation. |
|---|---|
| Authors: | Zhu Y; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Wang Y; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Zhang S; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Yang J; Department of Health Management Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China., Zhang F; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Liu Y; Department of Health Management Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China., Shang J; Department of Nephrology, The Second Hospital of Anhui Medical University, Hefei, Anhui, China., Zhang Y; Department of Health Management Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China., Wang J; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Liu L; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China. |
| Source: | Journal of medical Internet research [J Med Internet Res] 2026 Jun 19; Vol. 28, pp. e82529. Date of Electronic Publication: 2026 Jun 19. |
| Publication Type: | Journal Article; Validation Study |
| Language: | English |
| Journal Info: | Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2011- > : Toronto : JMIR Publications Original Publication: [Pittsburgh, PA? : s.n., 1999- |
| MeSH Terms: | Fatty Liver*/diagnosis , Fatty Liver*/pathology , Boosting Machine Learning Algorithms*, Adult ; Female ; Humans ; Male ; Middle Aged ; Predictive Learning Models ; Random Forest ; Severity of Illness Index ; Support Vector Machine ; Multilayer Perceptrons ; Bayes Theorem |
| Abstract: | Background: Steatotic liver disease affects 40% of nonobese individuals, but existing screening tools inadequately detect and stage disease severity in this population because of the limited sensitivity of conventional ultrasound and the lack of dedicated prediction models. Objective: This study aimed to develop and validate an interpretable machine learning model specifically for multiclass hepatic steatosis severity prediction in nonobese individuals to support early risk stratification in this underrecognized group. Methods: Health examination data from 215,145 nonobese participants (BMI <28 kg/m²) were randomly divided into training (n=150,601, 70%) and test (n=64,544, 30%) sets. Hepatic steatosis was diagnosed and graded using the controlled attenuation parameter with established thresholds (none: <248 dB/m; mild: 248-268 dB/m; and moderate to severe: >268 dB/m). From 42 candidate variables, 14 predictors were selected using Least Absolute Shrinkage and Selection Operator regression and Recursive Feature Elimination based on Random Forest importance. Six machine learning algorithms-k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, support vector machine, and Extreme Gradient Boosting (XGBoost)-were developed using 10-fold cross-validation, with hyperparameters optimized for maximal area under the receiver operating characteristic curve (ROC-AUC). Model interpretability was assessed using Shapley Additive Explanations analysis. External validation was conducted in non-Hispanic Asian participants from the National Health and Nutrition Examination Survey (n=726). Model performance was evaluated using accuracy, Cohen κ, ROC-AUC, area under the precision-recall curve, F1-score, precision, sensitivity, and specificity. Results: The final cohort included 215,145 participants, with steatosis severity classified as none (n=92,944, 43.2%), mild (n=54,121, 25.2%), and moderate to severe (n=68,080, 31.6%). Among the 6 machine learning models, XGBoost achieved the best discrimination on the test set, with an accuracy of 0.824 and a macro-average ROC-AUC of 0.941. In external validation, the model maintained strong performance (macro-average ROC-AUC=0.874). Shapley Additive Explanations analysis identified BMI, waist circumference, liver enzymes (alanine aminotransferase and aspartate aminotransferase), renal function indicators (uric acid and serum creatinine), and metabolic indices (triglycerides, continuous metabolic syndrome score, and triglyceride-glucose index) as key contributors to model predictions. The model has been implemented as an online prediction platform to facilitate clinical use. Conclusions: This interpretable XGBoost model accurately predicts controlled attenuation parameter-defined hepatic steatosis severity in nonobese individuals and demonstrates robust performance in both internal and external validation cohorts, providing a practical tool for early risk stratification in this underrecognized population. (© Yitong Zhu, Yongshuai Wang, Shenyu Zhang, Jian Yang, Feng Zhang, Yan Liu, Jun Shang, Yongliang Zhang, Jizhou Wang, Lianxin Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org).) |
| Contributed Indexing: | Keywords: controlled attenuation parameter; hepatic steatosis; machine learning; nonobese population; risk stratification; steatotic liver disease |
| Entry Date(s): | Date Created: 20260619 Date Completed: 20260620 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13282044 |
| DOI: | 10.2196/82529 |
| PMID: | 42320028 |
| Database: | MEDLINE |
| ISSN: | 1438-8871 |
|---|---|
| DOI: | 10.2196/82529 |