Research on physical health early warning based on GM(1,1)

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dc.cclicenceCC-BY-NCen
dc.contributor.authorZeng, Bo
dc.contributor.authorYang, Yingjie
dc.contributor.authorGou, Xiaoyi
dc.date.acceptance2022-01-20
dc.date.accessioned2022-05-12T13:09:26Z
dc.date.available2022-05-12T13:09:26Z
dc.date.issued2022-01-22
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractAt present, hundreds of millions of Chinese people face increasingly serious health risks, and health checks have undoubtedly played a significant role in finding health risks. However, the current health check in China mainly judges the quality of physical functions by a single index value without dynamic analysis of the changing trends of the index, which may lead to unreasonable diagnostic conclusions. In this paper, the data characteristics of physical indicators are systematically analyzed, and grey system models dedicated to data with the character- istics are applied to simulate and predict the changing trends of body indicators. On this basis, possible path- ological changes in body organs were identified. Specifically, this paper analyses the state of human kidney functions by grey prediction models. The results showed that even when the renal function index (serum creatinine) is within the normal range, the human renal function might be abnormal. The grey model analysis of the change trends of serum creatinine can predict the potential health hazards of renal functionsen
dc.exception.ref2021codes255aen
dc.funderOther external funder (please detail below)en
dc.funder.otherRoyal Societyen
dc.identifier.citationZeng, B., Yang, Y. and Gon, X. (2022) Research on physical health early warning based on GM(1,1). Computers in Biology and Medicine,143, 105256.en
dc.identifier.doihttps://doi.org/10.1016/j.compbiomed.2022.105256
dc.identifier.issn0010-4825
dc.identifier.urihttps://hdl.handle.net/2086/21871
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidIEC\NSFC\170391en
dc.publisherElsevieren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectEarly warning of body lesion trendsen
dc.subjectData characteristics of physical indicatorsen
dc.subjectGrey prediction modelsen
dc.subjectSerum creatinine and renal functionsen
dc.titleResearch on physical health early warning based on GM(1,1)en
dc.typeArticleen

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