An interpretable machine learning model for predicting metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes.

Abstract

Aim: Patients with type 2 diabetes mellitus (T2DM) exhibit an elevated prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and are at greater risk of liver-related adverse events. Existing non-invasive tools show limited diagnostic performance in this population. This study aims to develop a predictive model that accurately identifies the risk of MASLD among T2DM patients.

Materials and methods: Clinical data were collected from T2DM patients hospitalised at Nanjing Drum Tower Hospital between January 2018 and May 2025. Eight machine learning methods were developed to predict the risk of MASLD in T2DM patients. The discriminatory ability of the models was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, recall, negative predictive value, positive predictive value, and F1 score. Calibration curves and decision analysis curves were employed to evaluate the calibration and clinical utility. The models were interpreted using the Shapley additive explanations method, and unsupervised clustering was performed to identify potential high-risk subgroups.

Results: A total of 3836 T2DM patients constituted the complete dataset, with a MASLD incidence rate of 55.9%. Thirteen feature variables were selected for model construction, and the XGB model achieved optimal overall performance, with an AUROC of 0.873 and an AUPRC of 0.904. Unsupervised clustering identified several high-risk subgroups with distinct metabolic characteristics.

Conclusion: The model developed enables reliable and interpretable MASLD risk prediction in T2DM patients based on selected commonly available clinical data, providing a practical tool for routine identification and stratified management.