Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia.

Journal: Diabetes/metabolism research and reviews
PMID: 39497474

Abstract

BACKGROUND: Prediabetes and diabetes are both abnormal states of glucose metabolism (AGM) that can lead to severe complications. Early detection of AGM is crucial for timely intervention and treatment. However, fasting blood glucose (FBG) as a mass population screening method may fail to identify some individuals who are actually AGM but with normoglycemia. This study aimed to develop and validate machine learning (ML) models to identify AGM among individuals with normoglycemia using routine health check-up indicators.

Authors

  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Weidong Yao
    Department of Anesthesiology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Wenqi Hu
    Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany.
  • Guang Zhang
    Department of Health Management, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
  • Yongsheng Zhang
    Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, China.

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