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KMID : 1040620220280010105
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Clinical and Molecular Hepatology
2022 Volume.28 No. 1 p.105 ~ p.116
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Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods
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Lee Seung-Mi
Hwangbo Su-Hyun
Norwitz Errol R.
Koo Ja-Nam
Oh Ig-Hwan
Choi Eun-Saem
Jung Young-Mi
Kim Sun-Min
Kim Byoung-Jae
Kim Sang-Youn
Kim Gyoung-Min
Kim Won
Joo Sae-Kyung
Shin Sue
Park Chan-Wook
Park Tae-Sung
Park Joong-Shin
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Abstract
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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 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.
Results: Among 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=not significant between settings 4 and 5).
Conclusions: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144)
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KEYWORD
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Nonalcoholic fatty liver disease, Diabetes, Gestational, Machine learning, Prediction, Pregnancy, High-risk
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