Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning

Yan-Ting Wu, Chen-Jie Zhang, Ben Mol, Andrew Kawai, Cheng Li, Lei Chen, Yu Wang, Jian-Zhong Sheng, Jian-Xia Fan, Yi Shi* (Corresponding Author), He-Feng Huang* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

CONTEXT: Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking.

OBJECTIVES: This work aimed to establish effective models to predict early GDM.

METHODS: Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively.

RESULTS: A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66).

CONCLUSIONS: We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.

Original languageEnglish
Pages (from-to)e1191-e1205
Number of pages15
JournalJournal of Clinical Endocrinology and Metabolism
Volume106
Issue number3
Early online date22 Dec 2020
DOIs
Publication statusPublished - 31 Mar 2021

Keywords

  • GDM
  • early prediction
  • machine learning models
  • early pregnancy
  • BMI
  • thyroxine
  • HEMOGLOBIN
  • CLASSIFICATION
  • RISK
  • PREGNANCY
  • 1ST
  • DISCRIMINATION
  • GLUCOSE
  • INSULIN-RESISTANCE
  • INTRAUTERINE EXPOSURE
  • ASSOCIATION

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