ArticleFrontiers in public health2026
The application of explainable machine learning for the identification of MASLD in non-obese populations.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) in non-obese individuals presents a significant challenge, and its early identification is of considerable public health importance. This study aimed to develop and validate interpretable machine learning models using routine clinical parameters to identify MASLD in this specific population. Methods: Data from 768 non-obese subjects were analyzed. The dataset was randomly split into training ( Results: LASSO regression identified eight factors: apolipoprotein B, triglycerides, fasting insulin, fasting plasma glucose, waist circumference, age, alkaline phosphatase, and systolic blood pressure. On the test set, GBM achieved the highest AUC of 0.779 (95% CI: 0.695-0.863), followed by NN (0.777) and LDA (0.771). Both GBM and LDA demonstrated acceptable calibration (Hosmer-Lemeshow Conclusion: This study developed interpretable machine learning models (LDA and GBM) for identifying MASLD in non-obese individuals using readily available clinical parameters. The models demonstrated acceptable discrimination and calibration, with SHAP analysis highlighting the central roles of insulin resistance and dyslipidemia in the pathogenesis of non-obese MASLD.
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