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

Date

2022-01-22

Advisors

Journal Title

Journal ISSN

ISSN

0010-4825

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

At 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 functions

Description

The 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.

Keywords

Early warning of body lesion trends, Data characteristics of physical indicators, Grey prediction models, Serum creatinine and renal functions

Citation

Zeng, 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.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)