Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China

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...

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Detalles Bibliográficos
Autores principales: Gan, Ruijing, Chen, Xiaojun, Yan, Yu, Huang, Daizheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4357037/
https://www.ncbi.nlm.nih.gov/pubmed/25815044
http://dx.doi.org/10.1155/2015/328273
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author Gan, Ruijing
Chen, Xiaojun
Yan, Yu
Huang, Daizheng
author_facet Gan, Ruijing
Chen, Xiaojun
Yan, Yu
Huang, Daizheng
author_sort Gan, Ruijing
collection PubMed
description 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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spelling pubmed-43570372015-03-26 Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China Gan, Ruijing Chen, Xiaojun Yan, Yu Huang, Daizheng Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2015 2015-02-26 /pmc/articles/PMC4357037/ /pubmed/25815044 http://dx.doi.org/10.1155/2015/328273 Text en Copyright © 2015 Ruijing Gan et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Gan, Ruijing
Chen, Xiaojun
Yan, Yu
Huang, Daizheng
Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China
title Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China
title_full Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China
title_fullStr Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China
title_full_unstemmed Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China
title_short Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China
title_sort application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis b in china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4357037/
https://www.ncbi.nlm.nih.gov/pubmed/25815044
http://dx.doi.org/10.1155/2015/328273
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