Zeng Bo, Yang Yingjie, Gou Xiaoyi
School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
Centre for Computational Intelligence, De Montfort University, Leicester, LE1 9BH, UK.
Comput Biol Med. 2022 Apr;143:105256. doi: 10.1016/j.compbiomed.2022.105256. Epub 2022 Jan 22.
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.
目前，数亿中国人面临着日益严峻的健康风险，而健康检查在发现健康风险方面无疑发挥了重要作用。然而，当前中国的健康检查主要通过单一指标值来判断身体机能的质量，而没有对指标的变化趋势进行动态分析，这可能导致不合理的诊断结论。本文系统地分析了身体指标的数据特征，并应用针对具有该特征的数据的灰色系统模型来模拟和预测身体指标的变化趋势。在此基础上，识别出身体器官可能发生的病理变化。具体而言，本文通过灰色预测模型分析人类肾功能的状态。结果表明，即使肾功能指标（血清肌酐）在正常范围内，人类肾功能仍可能异常。对血清肌酐变化趋势的灰色模型分析可以预测肾功能的潜在健康危害。
