ArticleJournal of diabetes research2026
Machine Learning-Based Risk Stratification for Metabolic Dysfunction Severity Among Diabetic Patients With Established MASLD: Distinguishing Normal-Weight From Overweight Individuals.
Article in Journal of diabetes research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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6 authors.
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Abstract
objectiveTo rank biochemical and clinical features that distinguish normal-weight (BMI 18-24 kg/m
methodsThis study collected a total of 3524 samples from hospitalized patients with diabetes and nonalcoholic fatty liver disease (NAFLD), with 54 NAFLD features serving as the original dataset. After data preprocessing, 2624 samples and 52 NAFLD features were screened from the original dataset to form the final dataset for model input. Among these, 1848 patients were labeled as Class 0 (BMI > 24 kg/m
resultsUric acid, vitamin D, hemoglobin, and creatine kinase are the most significant features in normal-weight diabetes patients with MASLD. Gradient boosting was considered the best model; the average area under the ROC curve (AUC) was 0.733 (95% CI: 0.7089-0.7578).
conclusionGradient boosting is the optimal predictive model, which can assist healthcare professionals in risk assessment and management for diabetic MASLD patients with normal BMI.
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