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 p

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC12673455 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature