Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods.

ABSTRACT:

Background/aims

To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model.

Methods

This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10-14 weeks and screened them for GDM at 24-28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 var

SUBMITTER: Lee SM 

PROVIDER: S-EPMC8755469 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature