Volume 21, Issue 74 (1-2019)
jha 2019, 21(74): 51-64 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Farzin M, Afsar A, Dabir A, Zandi E. Hybrid modeling for forecasting domestic medical tourism demand in Tehran. jha 2019; 21 (74) :51-64
URL: http://jha.iums.ac.ir/article-1-2791-en.html
URL: http://jha.iums.ac.ir/article-1-2791-en.html
1- Allameh Tabatabai University ,Teharan, Iran , b_farzin@yahoo.com
2- , Tarbiyat modares University, Teharan.Iran
3- , Allameh Tabatabai University, Teharan.Iran
4- , Allameh Tabatabai University,Teharan.Iran
2- , Tarbiyat modares University, Teharan.Iran
3- , Allameh Tabatabai University, Teharan.Iran
4- , Allameh Tabatabai University,Teharan.Iran
Abstract: (5238 Views)
Introduction: One of the most important events in the tourism industry of each country is the demand for a product or destination and its true prediction of tourism. It should be noted that there are distances and deviations between actual values and predictions. The use of modern scientific and forecasting methods will make the results far more than an objective estimate and closer to the truth; this article pursues the same goal in the field of medical tourism.
Methods: In the first step, factors affecting the demand for domestic medical tourism in Tehran were identified by 31 experts using Fuzzy Delphi and Dematel Fuzzy methods. The factors were then processed by MATLAB2017a software. After determining the demand function, and collecting monthly data of each effective factor from 2001 to 2015, three regression prediction models, a fuzzy neural network, and SVR algorithm were implemented using MATLAB software to measure and compare forecast errors.
Results: The demand function for domestic medical tourism included: economic factors (individual income and wealth), service prices and cost of living in the destination, the cost of accommodation facilities, air pollution, and the price of alternative products (foreign travel), the number of medical centers, hospitals and laboratories.
Conclusion: The proposed hybrid approach for regression and SVR algorithm can make a better prediction compared with the other methods of forecasting domestic medical tourism. Therefore, it is recommended to use the demand function and forecasting model to lower the forecast error while planning for domestic medical tourism demand in Tehran.
Methods: In the first step, factors affecting the demand for domestic medical tourism in Tehran were identified by 31 experts using Fuzzy Delphi and Dematel Fuzzy methods. The factors were then processed by MATLAB2017a software. After determining the demand function, and collecting monthly data of each effective factor from 2001 to 2015, three regression prediction models, a fuzzy neural network, and SVR algorithm were implemented using MATLAB software to measure and compare forecast errors.
Results: The demand function for domestic medical tourism included: economic factors (individual income and wealth), service prices and cost of living in the destination, the cost of accommodation facilities, air pollution, and the price of alternative products (foreign travel), the number of medical centers, hospitals and laboratories.
Conclusion: The proposed hybrid approach for regression and SVR algorithm can make a better prediction compared with the other methods of forecasting domestic medical tourism. Therefore, it is recommended to use the demand function and forecasting model to lower the forecast error while planning for domestic medical tourism demand in Tehran.
Keywords: Regression, Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Regression (SVR) Algorithm, Forecasting of Domestic Medical Tourism Demand
Type of Study: Research |
Received: 2018/08/18 | Accepted: 2019/01/23 | Published: 2019/01/23
Received: 2018/08/18 | Accepted: 2019/01/23 | Published: 2019/01/23
References
1. Inskeep E. Tourism planning is an integrated and sustainable approach to tourism planning and development [Hasanpoor M and Daghestani S, trans]. Tehran: Mahkameh; 2004. [In Persian].
2. Khodayar Zarnagh R, Tourani S, Ghaderi A, Salehi M, Jafari H. Evaluation of the capabilities of selected educational hospitals of Tehran university of medical sciences in attracting medical tourists based on patient-centered standards of the joint international commission. Journal of Hospital.2010;3(4): 125-131. [In Persian].
3. Goodarzi M, Taghvaei M, Zangi Abadi A. Development of internal medicine tourism in Shiraz. Health Information Management Quarterly.2014; 11(4): 53-64. [In Persian].
4. Lohmann M. New Demand factors in tourism. Presented to the European Tourism Forum, Budapest.2004; 14(3):345-351.
5. Weng G, Li L. Study of tourism forecasting based on a seasonally adjusted particle swarm optimization-support vector regression model. JICS.2015; 12(7):2747-2757. [DOI:10.12733/jics20105860]
6. Shen S, Li G, Song H. Combination forecasts of international tourism demand. Annals of Tourism Research. 2011; 38(4): 72–89. [DOI:10.1016/j.annals.2010.05.003]
7. Song H, Turne L. Tourism demand forecasting. In: Dwyer L, Forsyth P, editors, International handbook on the economics of tourism. Cheltenham: Edward Elgar;2006. [DOI:10.4337/9781847201638.00010]
8. Claveria O, Torra A. Forecasting tourism demand to Catalonia: neural networks vs. Time Series Models, Economic Modeling.2014; 36(2):220-228. [DOI:10.1016/j.econmod.2013.09.024]
9. Can V. Modeling tourism demand, travel mode choice and destination loyalty [PhD thesis]. Norway: Faculty of Biosciences, Fisheries and Economics, Troms University Business School;2013.
10. Makridakis S, Hibon M. The M٣-competition: Results, conclusions and implications. IJF. 2000; 16(4):451-476.
11. Song H, Turne L. Tourism demand forecasting. In: Dwyer L, Forsyth P, editors, International handbook on the economics of tourism. Cheltenham: Edward Elgar.2006. [DOI:10.4337/9781847201638.00010]
12. Law R, Au N. A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management journal. 1999; 20(12):89-97.
13. Cho V. A comparison of three different approaches to tourist arrival forecasting. Tourism Management journal.2003; 24(12): 323–330. [DOI:10.1016/S0261-5177(02)00068-7]
14. Palmer A, Jose Montano J, Sese A. Designing an artificial neural network for forecasting tourism time series. Tourism Management journal. 2006; 27(4):781-790. [DOI:10.1016/j.tourman.2005.05.006]
15. Yepremian G. Forecasting tourism demand in Japon, International journal of forecasting.2005; 12(3):447-75.
16. Chen K, Wang C. Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management journal.2007; 28(2): 215–216.
17. Pai P, Hong W, Chang P, Chen T. The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study. IJM. 2006; 23(9): 375–385.
18. Shen S, Li G, Song H. Combination forecasts of international tourism demand. Annals of Tourism Research .2011; 38(4): 72–89 [DOI:10.1016/j.annals.2010.05.003]
19. Akin M. A novel approach to model selection in tourism demand modeling. Tourism Management journal.2015; 48(9):112-125.
20. Cheng C, Lin Y. Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation European. IJOR. 2002; 142(4): 147-186.
21. Rostamzadeh R, Sofian S. Prioritizing effective 7 Ms to improve production systems performance using fuzzy AHP and fuzzy Topsis (case study), Expert system with Applications.2011; 38(12); 5166-5177. [DOI:10.1016/j.eswa.2010.10.045]
22. Line S. A passion for oysters: the art of eating and enjoying. Reed International. 2010; 32(4):77-81.
23. Fahimifard M, Salarpur M, Soboohi M. Comparison of neuro-fuzzy model forecast prediction with neural network model and ARIMA self-regression, case study: Weekly Egg Prices.JAED. 2012; 32(74) : 138-145. [In Persian].
24. Alvani M, Mirshfi N. Production management. Mashhad: astan quds razavi Publishing House; 2000.[In Persian].
25. Vapnik V. The nature of statistical learning theory. New York: Springer;1995. [DOI:10.1007/978-1-4757-2440-0]
26. Helaly H, El-Shishiny H. An econometric forecasting model for tourism demand in Egypt. Proceedings of the Eurochrie Conference;2002.p. 651-661.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Site Keywords
Health, Administration, Scientific Article, Iran University of Medical Sciences,
