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Abstract
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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 BIPDEA
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.
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