{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T05:42:06Z","timestamp":1776058926475,"version":"3.50.1"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to develop and evaluate machine learning (ML) models for predicting non-alcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM) using readily accessible clinical and biochemical indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 2,459 patients with T2DM were enrolled in this cross-sectional study. Eight ML algorithms, logistic regression (LG), k-nearest neighbors (k-NN), support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and na\u00efve Bayes (NB), were developed to construct predictive models. Feature selection was performed using Boruta, recursive feature elimination, and LASSO regression. Model performance was assessed using several metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, recall, F1 score, and decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the study population, 1,309 individuals (53.23%) were diagnosed with NAFLD. Sixteen variables, including BMI, waist circumference, systolic blood pressure, triglycerides, HDL-C, ALT, GGT, bilirubin fractions, albumin, BUN, GFR, fasting insulin, RBC, and hemoglobin, were selected as key predictors. The SVM model demonstrated the best overall performance, achieving an AUC of 0.920, accuracy of 0.839, and specificity of 0.898 in the training set, and an AUC of 0.833 and accuracy of 0.733 in the validation set. Decision curve analysis confirmed superior clinical utility of the SVM model compared with other algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML-based models, particularly the SVM algorithm, effectively predicted NAFLD among patients with T2DM using easily accessible clinical and biochemical indicators. These findings highlight the potential utility of ML-assisted screening tools for improving early identification and risk stratification of NAFLD in diabetic populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1761846","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:44:15Z","timestamp":1776055455000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based prediction of NAFLD in patients with type 2 diabetes using routine clinical and biochemical indicators"],"prefix":"10.3389","volume":"17","author":[{"given":"Yongjun","family":"Zeng","sequence":"first","affiliation":[{"name":"Department of Cardiovascular, The First Affiliated Hospital, Fujian Medical University","place":["Fuzhou, China"]},{"name":"Department of Cardiovascular, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical 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