Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Abstract

BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. OBJECTIVE: The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions. METHODS: We performed a retrospective, observational study including women who attended their routine first hospital visits

Abstract by Yunzhen Ye; Yu Xiong; Qiongjie Zhou; Jiang‐Nan Wu; Xiaotian Li; Xirong Xiao, Journal of Diabetes Research (2020) — licensed CC BY 4.0.

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