Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model

He Huang, Liwei Zhong (), Ting Shen and Huixin Wang
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He Huang: University of Shanghai for Science and Technology
Liwei Zhong: Shanghai University of Traditional Chinese Medicine
Ting Shen: Shanghai University of Traditional Chinese Medicine
Huixin Wang: Shanghai University of Traditional Chinese Medicine

Journal of Combinatorial Optimization, 2022, vol. 44, issue 5, No 27, 3778-3791

Abstract: Abstract The coronavirus disease (COVID-19) pandemic has caused significant changes in the external environment of enterprises, resulting in tremendous negative impacts. Accordingly, the irregular fluctuation of business data poses a critical challenge to traditional approaches. Therefore, to combat the effects of the COVID-19 pandemic, an effective model is required to proactively predict an enterprise’s performance and simultaneously generate scientific performance optimization solutions. Consequently, at the intersection of artificial intelligence algorithms, operations research, and management science, an intelligent DEA-SVM model, which has a theoretical contribution, is developed in this study. The capabilities of this model are verified through sufficient numerical experiments. On the one hand, this model outperforms traditional algorithms in prediction accuracy. On the other hand, effective performance optimization solutions for low-performance enterprises are obtained from the input–output perspective. Moreover, the application value of this model is reflected in its successful implementation in the healthcare industry. Thus, it is a user-friendly tool for realizing the stable operation of enterprises in the context of the COVID-19 pandemic.

Keywords: SVM algorithm; Data envelopment analysis; Healthcare management; Performance prediction; Performance optimization (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10878-022-00911-9

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