A correlation-based feature analysis of physical examination indicators can help predict the overall underlying health status using machine learning.

Journal: Scientific reports
PMID: 36379988

Abstract

As a systematic investigation of the correlations between physical examination indicators (PEIs) is lacking, most PEIs are currently independently used for disease warning. This results in the general physical examination having limited diagnostic values. Here, we systematically analyzed the correlations in 221 PEIs between healthy and 34 unhealthy statuses in 803,614 individuals in China. Specifically, the study population included 711,928 healthy participants, 51,341 patients with hypertension, 12,878 patients with diabetes, and 34,997 patients with other unhealthy statuses. We found rich relevance between PEIs in the healthy physical status (7662 significant correlations, 31.5%). However, in the disease conditions, the PEI correlations changed. We focused on the difference in PEIs between healthy and 35 unhealthy physical statuses and found 1239 significant PEI differences, suggesting that they could be candidate disease markers. Finally, we established machine learning algorithms to predict health status using 15-16% of the PEIs through feature extraction, reaching a 66-99% accurate prediction, depending on the physical status. This new reference of the PEI correlation provides rich information for chronic disease diagnosis. The developed machine learning algorithms can fundamentally affect the practice of general physical examinations.

Authors

  • Haixin Wang
    Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Ping Shuai
    Health Management Center and Physical Examination Center of Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Yanhui Deng
    Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Jiyun Yang
    Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Yi Shi
    College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, People's Republic of China.
  • Dongyu Li
  • Tao Yong
    Medical Information Center of Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Yuping Liu
    Health Management Center and Physical Examination Center of Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. liuyuping555@126.com.
  • Lulin Huang
    Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. huangluling@yeah.net.

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