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Management of health care expenditure by soft computing methodology

Author

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  • Maksimović, Goran
  • Jović, Srđan
  • Jovanović, Radomir
  • Aničić, Obrad

Abstract

In this study was managed the health care expenditure by soft computing methodology. The main goal was to predict the gross domestic product (GDP) according to several factors of health care expenditure. Soft computing methodologies were applied since GDP prediction is very complex task. The performances of the proposed predictors were confirmed with the simulation results. According to the results, support vector regression (SVR) has better prediction accuracy compared to other soft computing methodologies. The soft computing methods benefit from the soft computing capabilities of global optimization in order to avoid local minimum issues.

Suggested Citation

  • Maksimović, Goran & Jović, Srđan & Jovanović, Radomir & Aničić, Obrad, 2017. "Management of health care expenditure by soft computing methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 370-373.
    • Handle: RePEc:eee:phsmap:v:465:y:2017:i:c:p:370-373
    DOI: 10.1016/j.physa.2016.08.035
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    References listed on IDEAS

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    Cited by:

    1. Chen, Yi & Ding, Shuai & Zheng, Handong & Zhang, Youtao & Yang, Shanlin, 2018. "Exploring diffusion strategies for mHealth promotion using evolutionary game model," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 148-161.

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