An early prediction model for gestational diabetes mellitus created using machine learning algorithms.

Journal: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Published Date: Mar 4, 2025

Abstract

OBJECTIVE: To investigate high-risk factors for gestational diabetes mellitus (GDM) in early pregnancy through an analysis of demographic and clinical data, and to develops a machine-learning-based prediction model to enhance early diagnosis and intervention.

Authors

  • Zhifen Yang
    Obstetrical Department, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
  • Xiaoyue Shi
    School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei Province, China.
  • Shengpu Wang
    Obstetrical Department, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
  • Lijia Du
    Obstetrical Department, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
  • Xiaoying Zhang
    College of Veterinary Medicine, Northwest A&F UniversityYangling, China; Chinese-German Joint Laboratory for Natural Product Research, Qinling-Bashan Mountains Bioresources Comprehensive Development C.I.C., College of Biological Science and Engineering, Shaanxi University of TechnologyHanzhong, China.
  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
  • Yongqiang Zhang
    School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China.
  • Jinlong Ma
    School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China.
  • Rui Zheng
    HKUST-DT System and Media Laboratory, Hong Kong University of Science and Technology, HongKong.

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