Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors.

Journal: BMC medical informatics and decision making
PMID: 39039521

Abstract

BACKGROUND: Diabetic peripheral neuropathy (DPN) and lower extremity arterial disease (LEAD) are significant contributors to diabetic foot ulcers (DFUs), which severely affect patients' quality of life. This study aimed to develop machine learning (ML) predictive models for DPN and LEAD and to identify both shared and distinct risk factors.

Authors

  • Ya Wu
    MOE Key Laboratory of Materials Physics and Chemistry in Extraordinary Conditions, Shaanxi Key Laboratory of Macromolecular Science and Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi'an, 710072, P. R. China. zhangxuan@nwpu.edu.cn.
  • Danmeng Dong
    School of Medicine, Anhui University of Science and Technology, Huainan, China.
  • Lijie Zhu
    Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
  • Zihong Luo
    School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Xiaoyun Xie
    Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China. xiaoyunxietj@126.com.

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