ISSN: 2225-8329
Open access
Hospital managers need to allocate emergency department (ED) resources efficiently because of the gradual aging of the population, emergency services overcrowding problem is arising. Forecasting is a vital activity that instructs decision-makers in related research fields, such as industrial scientific planning, economics, and healthcare. Scientists have applied time series methods to daily patient number forecasting at ED. Traditional time series models usually use a single variable for forecasting, but noises caused by weather conditions change and environmental factors would be included in raw data. Low forecasting performance would be generated because of using complicated raw data in time series models. Further, traditional time series models cannot be utilized in all datasets because statistics models need to meet statistical assumptions. Multi-attribute data will usually produce high-dimensional data and increase the computational complexity in the data mining procedure. For overcoming these drawbacks above, this study proposes a hybrid random-forest model based on AR (autoregressive) and empirical mode decomposition (EMD). The proposed model utilizes EMD to decompose complicated raw data into correlations frequency components and uses the feature section method to reduce high-dimensional input data generated by EMD. Then, this study combines random forest method that can surmount the limitations of statistical methods (data need to obey some mathematical distribution) to forecast daily patient volumes. To verification, daily patient volumes in an emergency are collected as experimental datasets to evaluate the proposed model. Experimental results illustrate that the proposed model surpasses the listing models
1. An, X., Jiang, D., Zhao, M., Liu, C. (2012) Short-term prediction of wind power using EMD and chaotic theory, Commun. Nonlinear Sci. Numer. Simul. 17 1036-1042,
2. Aneeshkumar, A. S., Venkateswaran, C. J. (2015) Reverse Sequential Covering Algorithm for Medical Data Mining, Procedia Computer Science, 47, 109-117,
3. Arslan, A. K., Colak C., Sarihan, M. E. (2016) Different medical data mining approaches based prediction of ischemic stroke, Computer Methods and Programs in Biomedicine, 130, 87-92,
4. Asplin, B. R., Flottemesch, T. J., Gordon, B. R. (2006) Developing models for patient flow and daily surge capacity research. Acad Emerg Med; 13: 1109-1113.
5. Bollerslev, T. (1986) Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics. 31 307-327.
6. Box, G., Jenkins, G. (1976) Time series analysis: Forecasting and control, San Francisco: Holden-Day.
7. Breiman, L. (2001). Random forests, Mach. Learn. 45 (1) 5–32,.
8. Chang, B. R. (2008) Resolving the forecasting problems of overshoot and volatility clustering using ANFIS coupling nonlinear heteroscedasticity with quantum tuning. Fuzzy Sets and Systems, 159(23) 3183-3200.
9. Chen, S. M. (1996) Forecasting enrollments based on fuzzy time series, Fuzzy Sets Systems, 81, 311-319.
10. Chen, K. L., Yeh, C. C., Lu, T. (2012) Forecasting the output of Taiwan’s integrated circuit (IC) industry using empirical mode decomposition and support vector machines, Int. J. Phys. Sci. 3 (78) 5460-5467.
11. Cheng, C. H., Wei, L. Y. (2014) A novel time series model based on empirical mode decomposition for forecasting TAIEX, Economic Modelling, 136-141
12. Engle, R. F. (1982) Autoregressive conditional heteroscedasticity with estimator of the variance of United Kingdom inflation. Econometrica. 50(4) 987-1008.
13. Feng, F., Zhu, D., Jiang, P., Jiang, H. (2010) GA-EMD-SVR condition prediction for a certain diesel engine, 2010 Progn. Syst. Heal. Manag. Conf. PHM ‘10,
14. Fu, C. (2010) Forecasting exchange rate with EMD-based Support Vector Regression, in: 2010 Int. Conf. Manag. Serv. Sci. MASS,
15. Friedman, M. (1937), The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc.; 675-701.
16. Georgio, G., Guttmann, A., Doan, Q. H. (2017). Emergency Department Flow Measures for Adult and Pediatric Patients in British Columbia and Ontario: A Retrospective, Repeated Cross-Sectional Study, The Journal of Emergency Medicine, In press.
17. Hall, M. A. (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.
18. Hamida, H. B. H., Scalera, F. (2019)Threshold Mean Reversion and Regime Changes of Cryptocurrencies using SETAR-MSGARCH Models International Journal of Academic Research in Accounting, Finance and Management Sciences Vol. 9, No.3, pp. 221–229
19. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng Q. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, in: Proceedings of the royal society of London series a-mathematical physical and engineering sciences, series A, 454 903-995.
20. Huarng, K. H. (2001) Effective lengths of intervals to improve forecasting in fuzzy time series, Fuzzy Sets and Systems. 123 155-162.
21. Hwang, S. W., Lee, H. J. (2008) Development of a revisit prediction model for the outpatient in a hospital. J Korean Soc Med Inform; 14: 137-145.
22. Izadi, M., Taghva, M. R. (2017)Using AHP Technique and Fuzzy VIKOR Technique to Select a Dynamic Enterprise Resource Planning System in Rayan Pardazesh Co. Case study, International Journal of Academic Research in Accounting, Finance and Management Sciences Vol. 7, No.2, April, pp. 64–75
23. Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., Snow, G. L. (2008) Forecasting Daily Patient Volumes in the Emergency Department, Academic Emergency Medicine; 15:159–170
24. Kim, K., Han, I. (2000) Genetic al
To cite this article: Huang, D. H., Tsai, C. H., Chueh, H. E., Wei, L.. Y. (2019). A Hybrid Model Based on EMD-Feature Selection and Random Forest Method for Medical Data Forecasting, International Journal of Academic Research in Accounting, Finance and Management Sciences 9 (4): 241-252
Copyright: © 2020 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode