More Web Proxy on the site http://driver.im/

research-article

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

Authors:

Xiaoyi GouAuthors Info & Claims

Volume 143, Issue C

https://doi.org/10.1016/j.compbiomed.2022.105256

Published: 01 April 2022 Publication History

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 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.

Highlights

•

The six characteristics of physical examination index data are systematically analyzed.

•

The grey prediction model of index data is scientifically constructed.

•

This study proposed a research framework of health early warning based on grey theory.

•

The validity of the model and the scientificity of the health early warning were verified by the case study.

References

[1]

Guanghua Bost Consumption Data Center : Big Data on Chinese Health [EB/OL]. http://www.sohu.com/a/252732622_499095.

[2]

W. Lauren, B. Prachi, T. Nick, Comparing trends in mortality from cardiovascular disease and cancer in the United Kingdom, 1983-2013: joinpoint regression analysis, Popul. Health Metrics 15 (1) (2017) 23–31,.

[3]

V. Alunni, P. du Jardin, L. Nogueira, L. Buchet, G. Quatrehomme, Comparing discriminant analysis and neural network for the determination of sex using femur head measurements, Forensic Sci. Int. 253 (2015) 81–87,.

[4]

B. Zeng, H. Li, Prediction of coalbed methane production in China based on an optimized grey system model, Energy Fuels (35) (2021) 4333–4344,.

[5]

A. Singh, S.N. Mohapatra, Abheejeet. Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting, Renew. Energy 136 (2019) 758–768,.

[6]

Y.F. Wang, Z.M. Ma, C.N. Xu, Z.K. Wang, X.H. Yang, Prediction of transfer among multiple states of blood pressure based on Markov model: an 18-year cohort study, J. Hypertens. 36 (2018) 1506–1513,.

[7]

F.D.N. Neto, C.D. Baptista, C.E.C. Campelo, Combining Markov model and Prediction by Partial Matching compression technique for route and destination prediction, Knowl. Base Syst. 154 (2018) 81–92,.

Digital Library

[8]

Q. Wang, X.X. Song, R.R. Li, A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting US shale oil production, Energy 165 (2018) 1320–1331,.

[9]

X. Ma, A brief introduction to the grey machine learning, J. Grey Syst. 31 (2019) 1–12.

[10]

Z.X. Wang, Q. Li, Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model, J. Clean. Prod. 207 (2019) 214–224,.

[11]

J.Z. Wang, P. Du, H.Y. Lu, W.D. Yang, T. Niu, An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting, Appl. Soft Comput. 72 (2018) 321–337,.

[12]

S. Katayama, Computational and statistical analyses for robust non-convex sparse regularized regression problem, J. Stat. Plann. Inference 201 (2019) 20–31,.

[13]

D. Rothenhausler, P. Buhlmann, N. Meinshausen, Causal Dantzig: fast inference in linear structural equation models with hidden variables under additive interventions, Ann. Stat. 47 (2019) 1688–1722,.

[14]

M.J. Gai, S.W. Cui, S. Liang, X.L. Liu, Frequency distributed model of Caputo derivatives and robust stability of a class of multi-variable fractional-order neural networks with uncertainties, Neurocomputing 202 (2016) 91–97,.

Digital Library

[15]

B. Zeng, H.M. Duan, Y.F. Zhou, A new multivariable grey prediction model with structure compatibility, Appl. Math. Model. (75) (2019) 385–397,.

[16]

R.R. Dalmat, S.J. Mooney, P.M. Hurvitz, C. Zhou, A.V. Moudon, B.E. Saelens, Walkability measures to predict the likelihood of walking in a place: a classification and regression tree analysis, Health Place 72 (2021) 102700,.

[17]

T. Phillips, W. Abdulla, Developing a new ensemble approach with multi-class SVMs for Manuka honey quality classification, Appl. Soft Comput. 111 (2021) 107710,.

Digital Library

[18]

X. Ma, M. Xie, W.Q. Wu, B. Zeng, Y. Wang, X.X. Wu, The novel fractional discrete multivariate grey system model and its applications, Appl. Math. Model. 70 (2019) 402–424,.

[19]

S.F. Liu, Y. Lin, An Introduction to Grey Systems, IIGSS Academic Publisher, 1998.

[20]

B. Zeng, M.Y. Tong, X. Ma, A new-structure grey Verhulst model: development and performance comparison, Appl. Math. Model. 81 (2020) 522–537,.

[21]

P.P. Xiong, Y. Yin, J. Shi, H. Gao, Nonlinear multivariable GM (1, N) model based on interval Gray number sequence, J. Grey Syst. 30 (2018) 33–47.

[22]

J.L. Deng, The Control problem of grey systems, Syst. Control Lett. 1 (1982) 288–294,.

[23]

S.F. Liu, L.Y. Tao, N.M. Xie, Y.J. Yang, On the new model system and framework of grey system theory, J. Grey Syst. 28 (2016) 1–15.

[24]

S.F. Liu, Y. Lin, Grey Systems: Theory and Applications, Springer-Verlag Berlin Heidelberg, London, 2010.

[25]

Y. Fu, J.J. Yao, H.H. Zhao, G. Zhao, Z.S. Wan, Forecast for artificial muscle tremor behavior based on dynamic additional grey catastrophe prediction, Appl. Sci.-basel 8 (2018) 315,.

[26]

C.Z. Bao, M. Mayila, Z.H. Ye, J.B. Wang, M.J. Jin, W.J. He, K. Chen, Forecasting and analyzing the disease burden of aged population in China, based on the 2010 global burden of disease study, Int. J. Environ. Res. Publ. Health 12 (2015) 7172–7184,.

[27]

X.J. Shen, L.M. Ou, X.J. Chen, X. Zhang, X.R. Tan, The application of the grey disaster model to forecast epidemic peaks of typhoid and paratyphoid fever in China, PLoS One 8 (2013),.

[28]

R.J. Gan, X.J. Chen, Y. Yan, D.Z. Huang, Application of a Hybrid method combining grey model and Back propagation artificial neural networks to forecast hepatitis B in China, Comput. Math. Methods Med. (2015) 328273,.

[29]

X.N. Zhang, L. Zhang, Y.H. Zhang, Z.Y. Liao, J.L. Song, Predicting trend of early childhood caries in mainland China: a combined meta-analytic and mathematical modelling approach based on epidemiological surveys, Sci. Rep. 7 (2017) 6507,.

[30]

H.M. Qu, Y.N. Bai, F.R. Kui, X.B. Hu, H.B. Pei, X.W. Ren, X.P. Shen, Effect of combination model on fitting cancer mortality and prediction, Chin. J. Epidemiol. 38 (2017) 117–120,.

[31]

L.P. Zhang, Y.L. Zheng, K. Wang, X.L. Zhang, Y.J. Zheng, An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China, Comput. Biol. Med. 49 (2014) 67–73,.

[32]

X. Han, Y.Q. Liu, H. Gao, J.M. Ma, X.X. Mao, Y.T. Wang, X.D. Ma, Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis, Sci. Total Environ. 607 (2017) 1009–1017,.

[33]

B.X. Chen, J.X. Zhu, X.R. Tan, Grey relation between coronary lesions and serum lipoid substance, J. Grey Syst. 21 (2009) 351–356.

[34]

Q.Y. Zhang, X.Y. Zheng, D.Q. Yi, W.Y. Ji, S.R. Chen, Grey relation between glucose and lipid metabolism in type 2 diabetes mellitus with secondary malignancy, J. Grey Syst. 22 (2010) 187–194.

[35]

B. Zeng, S.F. Liu, Y. Bai, M. Zhou, Grey system modeling technology for early prediction and warning of human diseases, Chin. J. Manag. Sci. 28 (2020) 144–152,.

[36]

B. Zeng, Y.T. Tan, H. Xu, J. Quan, L.Y. Wang, X.Y. Zhou, Forecasting the electricity consumption of commercial sector in Hong Kong using a novel grey dynamic prediction model, J. Grey Syst. 30 (2018) 157–172.

[37]

B. Zeng, H.M. Duan, Y. Bai, W. Meng, Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator, Energy 151 (2018) 238–249,.

[38]

S.F. Liu, Y.J. Yang, Forrest, J. Grey, Data Analysis Methods, Models and Applications, Springer-Verlag Berlin Heidelberg, London, 2016.

[39]

J. Deng, Undulating grey model GM(1,1|tan(k-τ)p, sin(k-τ)p), J. Grey Syst. 13 (2001) 201–205.

[40]

B. Zeng, W. Meng, S.F. Liu, Research on prediction model of oscillatory sequence based on GM(1,1) and its application in electricity demand prediction, J. Grey Syst. 25 (2013) 31–40.

[41]

B. Zeng, W. Meng, Interval prediction modeling method of small sample oscillation sequence based on grey theory, Control Decis. 31 (2016) 1311–1316,.

Cited By

Cang HZeng XYan S(2024)A novel grey multivariate convolution model based on the improved marine predators algorithm for predicting fossil CO2 emissions in ChinaExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122865243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122865

Index Terms

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

Index terms have been assigned to the content through auto-classification.

Recommendations

Utilizing Narrative Text from Electronic Health Records for Early Warning Model of Chronic Disease
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Chronic diseases are associated with high morbidity and mortality, and they cannot be quickly determined and identified during their early stages. Therefore, early identification and warning of chronic diseases are important. This study proposed an early ...
Research and Design of Distribution Equipment Health Early Warning System
Algorithms and Architectures for Parallel Processing
Abstract
Load forecasting and health early warning of distribution equipment is a very active research field and an important aspect, which can guarantee the system stability under large disturbances and optimize the distribution of energy resources in the ...
Research and Design of Atrial Fibrillation Early Warning Service System Based on Mobile Internet
ICMHI '20: Proceedings of the 4th International Conference on Medical and Health Informatics

Atrial fibrillation is closely related to hypertension. In view of the few studies on the combination of atrial fibrillation and blood pressure, an atrial fibrillation sphygmomanometer which can measure both atrial fibrillation and blood pressure ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 143, Issue C

Apr 2022

829 pages

ISSN:0010-4825

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Cang HZeng XYan S(2024)A novel grey multivariate convolution model based on the improved marine predators algorithm for predicting fossil CO2 emissions in ChinaExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122865243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122865

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents