Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.

Journal: Journal of diabetes investigation
PMID: 38439210

Abstract

AIMS/INTRODUCTION: Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed the best and whether muscle strength provided any incremental benefit in identifying undiagnosed diabetes in Chinese adults.

Authors

  • Ying Xu
    School of Biological and Food Engineering Changzhou University Changzhou Jiangsu China.
  • Shanhu Qiu
    Department of General Practice, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.
  • Jinli Ye
    School of Mathematics and Statistics, Yunnan University, Kunming, China.
  • Dan Chen
    Department of Information Engineering, Southwest Jiaotong University Hope College, Chengdu, Sichuan, China.
  • Donglei Wang
    Department of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.
  • Xiaoying Zhou
    School of Information Resource Management, Renmin University of China, Beijing, China.
  • Zilin Sun
    Department of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.

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