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

Abstract

Background & Aims\nTo develop an early prediction model for gestational diabetes (GDM) using machine learning and evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model.\n\n\nMethods\nThis prospective cohort study evaluated pregnant women for NAFLD by 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 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks.\n\n\nResults\nAmong 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among Settings 1-4 was Setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563-0.697 in Settings 1-3 vs. 0.740-0.781 in Setting 4). Setting 5 with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to Setting 4 (AUC 0.719-0.819 in Setting 5, p=NS between Settings 4 and 5).\n\n\nConclusions\nWe developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction.