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. 2015:2015:328273.
doi: 10.1155/2015/328273. Epub 2015 Feb 26.

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china

Ruijing Gan  1 , Xiaojun Chen  1 , Yu Yan  1 , Daizheng Huang  1
Affiliations

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china

Ruijing Gan et al. Comput Math Methods Med. 2015.

Abstract

Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

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Figures

Figure 1
Figure 1
Flow chart of the hybrid method.
Figure 2
Figure 2
The incidence number of hepatitis B in China from 2002 to 2012.
Figure 3
Figure 3
The topology structure of the proposal method.
Figure 4
Figure 4
The forecasted incidence of hepatitis B in China from 2013 to 2021 by the proposal method.
Figure 5
Figure 5
The scatter diagram of the relationship between the observed data and the prediction.
Figure 6
Figure 6
Comparison of the RE of the prediction by the proposal method and the GMs.
Figure 7
Figure 7
The forecasted incidence of hepatitis B in China from 2013 to 2021 by the three methods.
Figure 8
Figure 8
The predicted incidence of hepatitis B in China from 2003 to 2012 by the proposal methods.
Figure 9
Figure 9
The forecasted incidence of hepatitis B in China from 2013 to 2021 by the proposal methods.
See this image and copyright information in PMC

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