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Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection

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  • Zeynep Ceylan
  • Abdulkadir Atalan

Abstract

This study presents a comprehensive analysis of artificial intelligence (AI) techniques to predict healthcare expenditure per capita (pcHCE) in Turkey. Well‐known AI techniques such as random forest (RF), artificial neural network (ANN), multiple linear regression (MLR), support vector regression (SVR), and relevance vector machine (RVM) were used to forecast pcHCE. Twenty‐nine years of historical data from 1990 to 2018 were used in the training and testing phases of the models. Gross domestic product per capita, life expectancy at birth, unemployment rate, crude birth rate, and the number of physicians and hospitals were used as input variables for the analysis. A genetic algorithm‐based feature selection (GAFS) method was applied to all models to select the relevant and optimal feature subset in the prediction of pcHCE. The comparative results showed that the GAFS method improved the overall performance of all base AI models. The hybrid GAFS‐RF model performed best among all AI‐based prediction methods, with a 99.86% correlation of determination (R2) value at the testing stage.

Suggested Citation

  • Zeynep Ceylan & Abdulkadir Atalan, 2021. "Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 279-290, March.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:2:p:279-290
    DOI: 10.1002/for.2747
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    References listed on IDEAS

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    1. Zeynep Ceylan, 2020. "Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 944-956, September.
    2. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    3. Braendle, Thomas & Colombier, Carsten, 2016. "What drives public health care expenditure growth? Evidence from Swiss cantons, 1970–2012," Health Policy, Elsevier, vol. 120(9), pages 1051-1060.
    4. Devdatta Ray & Mikael Linden, 2020. "Health expenditure, longevity, and child mortality: dynamic panel data approach with global data," International Journal of Health Economics and Management, Springer, vol. 20(1), pages 99-119, March.
    5. Roberto Astolfi & Luca Lorenzoni & Jillian Oderkirk, 2012. "A Comparative Analysis of Health Forecasting Methods," OECD Health Working Papers 59, OECD Publishing.
    6. Getzen, Thomas E. & Poullier, Jean-Pierre, 1992. "International health spending forecasts: Concepts and evaluation," Social Science & Medicine, Elsevier, vol. 34(9), pages 1057-1068, May.
    7. Clemente, Jesús & Lázaro-Alquézar, Angelina & Montañés, Antonio, 2019. "US state health expenditure convergence: A revisited analysis," Economic Modelling, Elsevier, vol. 83(C), pages 210-220.
    8. Livio Di Matteo, 2010. "The sustainability of public health expenditures: evidence from the Canadian federation," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(6), pages 569-584, December.
    9. Astolfi, Roberto & Lorenzoni, Luca & Oderkirk, Jillian, 2012. "Informing policy makers about future health spending: A comparative analysis of forecasting methods in OECD countries," Health Policy, Elsevier, vol. 107(1), pages 1-10.
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