Prediction of cardiovascular mortality based on Grey Markov model

Zikuan Wang, Haiwen Yu

Article ID: 1896
Vol 2, Issue 1, 2021
DOI: https://doi.org/10.54517/ccr.v1i1.1896
VIEWS - 25 (Abstract)

Abstract

With the development of society and economy, the number of cardiovascular diseases continues to increase. Accurate prediction of cardiovascular mortality can guide the prevention of cardiovascular events and the sustainable development of public health. This paper mainly studies the grey Markov prediction model of cardiovascular disease mortality, proposes an improved grey Markov prediction model, and then uses the statistical data of cardiovascular disease mortality in rural and urban areas from 1991 to 2018 for numerical simulation. The simulation results show that the improved grey Markov prediction model is effective.


Keywords

Grey Markov; GM (1,1) model; cardiovascular disease; forecast; simulation

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References

1. National Cardiovascular Center China cardiovascular disease report. Beijing: China Encyclopedia press; 2019.

2. Editorial staff of China Health Statistical Yearbook China Health Statistical Yearbook. Beijing: China Union Medical College Press; 2019.

3. Liliming, raokeqin, konglingzhi, et al Survey on nutrition and health status of Chinese residents in 2002. Chinese Journal of epidemiology 2005; 26(7): 478–484.

4. Liusifeng Grey system theory and its application. Beijing: Science Press; 2008.

5. Xiao Zhengming, Li Huan Application of Grey Theory in urban land price prediction -- a case study of Xiamen City. Jiangxi science 2011; 29(5): 663–666.

6. Sun Aimin Prediction of power demand in Xi'an Based on metabolic grey prediction model. Practice and understanding of mathematics 2019; 49(23): 298–305.

7. Huang Yinhua, Peng Jian, lichangchun, et al. Application of Markov theory in medium and long term load forecasting. Journal of power system and automation 2011; 23(5): 131–136.

8. Zhouzhijian, fuzetian, wangruimei, et al Application of Grey Markov model in cotton yield prediction. Statistics and decision making 2005; 183 (2): 48– 49.

9. Liu Miao Grey prediction model and its application in power demand. Xi’an: Xi’an University of architecture and technology; 2012.

10. Yellow steel Research on emergency blood support characteristics and demand prediction model for unconventional emergencies. Chengdu: Southwest Jiaotong University; 2012.

11. Editorial board of China Health Yearbook China Health Yearbook. Beijing: People’s Health Publishing House; 1992–2002.

12. Xuhuilin, Gao Xingjun. Numerical differentiation algorithm based on integral. Jiangxi science 2014; 32(1): 1–4.

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