Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model
Lean Yu 1 * , Geye Hang 1, Ling Tang 1, Yang Zhao 2, K. K. Lai 3
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1 School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, CHINA2 Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190, CHINA3 Department of Management Sciences, City University of Hong Kong, Kowloon, HONG KONG* Corresponding Author

This article belongs to the special issue "Problems of Application Analysis in Knowledge Management and Science-Mathematics-Education".


Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of “decomposition and ensemble”. In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.


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.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2017, Volume 13, Issue 12, 7615-7627

Publication date: 14 Nov 2017

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Article Downloads: 1279

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