Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.

Journal: Frontiers in endocrinology
Published Date: Jan 20, 2023

Abstract

OBJECTIVE: For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to predict blood glucose for type 2 diabetic patients. We investigated various parameters influencing blood glucose, as well as determined the most effective machine learning algorithm in predicting blood glucose.

Authors

  • Xiaomin Fu
    Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Yuhan Wang
    School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Ryan S Cates
    Department of Emergency Medicine Stanford Healthcare TriValley, Stanford University School of Medicine, Stanford, Pleasanton, CA, United States.
  • Nan Li
    School of Basic Medical Sciences, Jiamusi University No. 258, Xuefu Street, Xiangyang District, Jiamusi 154007, Heilongjiang, China.
  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Dianshan Ke
    Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China.
  • Jinghua Liu
    Department of Cardiology Beijing Anzhen Hospital Capital University of Medical Sciences Beijing 100029 China.
  • Hongzhou Liu
    Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Shuangtong Yan
    Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.

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