The Prediction of Iran's Per Capita Health Expenditures up to 2041 Horizon Using the Genetic and Particle Swarm Optimization Algorithms
Subject Areas : medical documentsabolghasem golkhandan 1 , Somayeh Sahraei 2
1 - Ph.D. Economics, Faculty of Economics and Administrative, Lorestan University, Khoram Abad, Iran
2 - M.A Economics, Science and Research Branch Khuzestan, Islamic Azad University, Ahvaz, Iran
Keywords: Health Expenditure, Iran, Prediction, Genetic Algorithm (GA), Particle Swarm Optimization (PSO),
Abstract :
Introduction: prediction the per capita health expenditures can be useful and effective in determining the best policies for financing and managing of health expenditures. Accordingly, the main objective of this study was to predict the per capita health expenditures trend in Iran. Methods: In this paper, we specified a health expenditure model relying on theoretical basics in order to obtain desirable forecasts. On the basis of three forms of linear, exponential and quadratic equations and using theoretical foundations in the field of per capita health expenditure function, we used genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to simulate Iranians per capita health expenditure during 1979-2015. Then we selected the superior model in terms of prediction power criteria and forecast per capita health expenditure until 2041. Also, the statistical analyzes were performed using the MATLAB software version R2016b. Results: The predicted results indicate that per capita health expenditures in Iran will increase with a positive slope by 2041. The amount of this expenditure will be from $ 1081 (based on 2011 constant prices) in 2015 to $ 2628 in 2041 (about 2.5 times). Conclusion: With regard to the projected amount of per capita health expenditures up to 2041 horizon, policy makers in the health sector should take the necessary measures to finance the expenditures of this sector.
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1- Mohseni M. Medical sociology. 5 ed. 5nd, editor. Tehran: Tahori Publication; 2009. [In Persian]
2- Alizadeh M and Golkhandan, A. Robust determinants of health sector costs in Iran: Bayesian model averaging approach. Journal of Health Care Management, 2017; 7(2): 47-61. [In Persian]
3- Newhouse J. Medical care expenditures; a cross national study, J Hum Resource, 1977; 12: 10-26.
4- Magazzino C and Mele M. The Determinants of health expenditure in Italian regions, International Journal of Economics & Finance, 2012; 4(3): 61-72.
5- Wang Z. The determinants of health expenditures: evidence from US state-level data, Applied Economics, 2009; 41(4), 429-435.
6- Ang J. B. The determinants of health care expenditure in Australia, Applied Economics Letters, 2009; 17(4): 639-644.
7- Bilgel F and Tran KC. The determinants of Canadian provincial health expenditures: evidence from a dynamic panel, 2012; 45(2): 201-212.
8- Fattahi M, Osari A, Sadegi H and Asgharpur H. Effects of air pollution on public spending for health: Comparative developing and developed countries, Journal of Economic Development, 2013; 3(11): 111-132. [In Persian]
9- Rezaei S, Dindar A, Rezapour A. health care expenditures and their determinants: Iran provinces (2006-2011), Journal of Health Administration, 2016; 19(63): 81-90. [In Persian]
10- Jalaei Esfandabadi A, Taleghani F, Mangali H and Aramesh H. Simulation and forecasting of non-oil exports to 1404 horizons, Quarterly journal of Economic Strategy, 2013; 4: 147-166. [In Persian]
11- Holland J H. Adaptation in Natural and Artificial Systems, University of Michigan Press; 1975.
12- Ghanbari A, Khezri M and Azami A. Simulation of oil and gas demand function in Iran's land transport using an algorithmic genetic method, Quarterly Journal of Quantitative Economics, 2008; 4: 157-177. [In Persian]
13- Chakraborty M and Chakraborty UK. An analysis of linear ranking and binary tournament selection in genetic algorithms, Communications and Signal Processing, 1997; 1: 407-411.
14- Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning; 1989.
15- Coir DW and Smith AE. Using a neural network as a function evaluator during GA search for reliability optimization, In Proceedings of the Artificial Neural Networks in Engineering, 1995; 5: 369-374.
16- Charbonneau P. An introduction to genetic algorithms for numerical optimization, NCAR Technical Note; 2002: 74.
17- Rudolph G. Convergence properties of canonical genetic algorithms, IEEE Transactions on Neural Networks, 1994; 1(5): 96–101.
18- Reeves CR, Scott D and Harrison A. Applying genetic algorithms to container transshipment, Artificial Neural Nets and Genetic Algorithms; 2003: 234-238.
19- Kennedy J and Eberhart R. Particle swarm optimization, Proc. Proceedings of IEEE International Conference on Neural Networks; 1995.
20- Bhmani M, Ghaseminejad A, Karimian A.K and Aramesh H. Simulation of the electric power demand in agricultural sector using the particle swarm algorithm, Journal of Agricultural Economics Research, 2014; 6(2): 1-11. [In Persian]
21- Rasel M and Ardalan A. The future of ageing and its health care costs: a warning for health system, sija, 2007; 2(2): 300-305. [In Persian]
22- Seyedzadehabras S, Delavari M and Babakhani M. The factors affecting on Iran’s health expenditure and forecasting based on dynamic systems model, Hakim Health Sys Res, 2018; 20(4): 240- 250. [In Persian]
23- Chaabouni S and Abednnadher C. Modelling and forecasting of Tunisia’s health expenditures using artificial neural network and ARDL models. Int J Med Sci Public Health, 2013; 2(3): 495-503.
24- Dieleman JL, Campbell M, Chapin A, et al. Future and potential spending on health 2015-40: development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries. Lancet, 2017; 389(10083): 2005-2030.
