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

被引：16

作者：

Zeng, Bo ^{[1
]}

Yang, Yingjie ^{[2
]}

Gou, Xiaoyi ^{[1
]}

机构：

[1] Chongqing Technol & Business Univ, Sch Management Sci & Engn, Chongqing 400067, Peoples R China

[2] De Montfort Univ, Ctr Computat Intelligence, Leicester LE1 9BH, Leics, England

来源：

COMPUTERS IN BIOLOGY AND MEDICINE | 2022年 / 143卷

基金：

中国国家自然科学基金;

关键词：

Early warning of body lesion trends; Data characteristics of physical indicators; Grey prediction models; Serum creatinine and renal functions; GREY MODEL; ELECTRICITY CONSUMPTION; PREDICTION; CHINA; OPTIMIZATION; ALGORITHM; SYSTEM;

D O I：

10.1016/j.compbiomed.2022.105256

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 characteristics are applied to simulate and predict the changing trends of body indicators. On this basis, possible pathological 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.

引用

页数：8

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