Data envelopment analysis using the binary-data
Journal of Modelling in Management
ISSN: 1746-5664
Article publication date: 8 March 2021
Issue publication date: 17 February 2022
Abstract
Purpose
The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming that all of the data are non-negative. However, there are situations where all data are binary. As an example, the information on many diseases in health care is binary data. The existence of binary data in traditional DEA models may change the behavior of the production possibility set (PPS). This study defines a binary summation operator, expresses the modified principles and introduces the extracted PPS of axioms. Furthermore, this study proposes a binary integer programming of DEA (BIP-DEA) for assessing the efficiency scores to use as an alternate tool in prediction.
Design/methodology/approach
In this study, the extracted PPS of modified axioms and the BIP-DEA model for assessing the efficiency score is proposed.
Findings
The binary integer model was proposed to eliminate the challenges of the binary-value data in DEA.
Originality/value
The importance of the proposed model for many fields including the health-care industry is that it can predict the occurrence or non-occurrence of the events, using binary data. This model has been applied to evaluate the most important risk factors for stroke disease and mechanical disorders. The targets set by this model can help to diagnose earlier the disease and increase the patients’ chances of recovery.
Keywords
Acknowledgements
The authors would like to thank Prof. Mehdi Farhoudi, head of Neurosciences Research Center, Tabriz University of Medical Sciences, for his efforts in the data gathering and the help during this research. It should be noted that Prof. Farhoudi confirmed the results of the study.
Citation
Pourmahmoud, J. and Gholam Azad, M. (2022), "Data envelopment analysis using the binary-data", Journal of Modelling in Management, Vol. 17 No. 1, pp. 49-65. https://doi.org/10.1108/JM2-10-2019-0246
Publisher
:Emerald Publishing Limited
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