Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.

Journal: BMC public health
PMID: 40140819

Abstract

OBJECTIVE: The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning methods have proven effective in enhancing prediction accuracy. However, existing approaches may lack interpretability regarding underlying mechanisms. Therefore, we aim to employ an interpretable machine learning approach utilizing nationwide cross-sectional data to predict pre-diabetic risk and quantify the impact of potential risks.

Authors

  • Xiaolong Li
    Auckland Tongji Medical & Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China.
  • Fan Ding
    School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
  • Lu Zhang
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Shi Zhao
    Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
  • Zengyun Hu
    School of Public Health, Shanghai Jiao Tong University, Shanghai, 200025, China.
  • Zhanbing Ma
    School of Basic Medicine, Ningxia Medical University, Yinchuan Ningxia, 750004, China.
  • Feng Li
    Department of General Surgery, Shanghai Traditional Chinese Medicine (TCM)-INTEGRATED Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Yuhong Zhang
    Department of Ultrasound, The Second Hospital of Dalian Medical University, Dalian, China.
  • Yi Zhao
    Department of Biostatistics and Health Data Science, Indiana University School of Medicine.
  • Yu Zhao
    College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.

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