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• W4309087246 abstract "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." @default.
• W4309087246 created "2022-11-21" @default.
• W4309087246 creator A5008683048 @default.
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• W4309087246 date "2022-11-15" @default.
• W4309087246 modified "2023-10-18" @default.
• W4309087246 title "A correlation-based feature analysis of physical examination indicators can help predict the overall underlying health status using machine learning" @default.
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• W4309087246 doi "https://doi.org/10.1038/s41598-022-20474-3" @default.
• W4309087246 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36379988" @default.
• W4309087246 hasPublicationYear "2022" @default.
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