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.

中文翻译：

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使用二进制数据进行数据包络分析

目的

本文的目的是提出可以用作二进制值数据的数据包络分析（DEA）模型。通常，基本 DEA 模型是通过假设所有数据都是非负数来开发的。但是，在某些情况下，所有数据都是二进制的。例如，医疗保健中许多疾病的信息都是二进制数据。传统 DEA 模型中二进制数据的存在可能会改变生产可能性集 (PPS) 的行为。本研究定义了一个二元求和算子，表达了修改后的原理，并介绍了公理的提取PPS。此外，本研究提出了 DEA 的二进制整数规划 (BIP-DEA)，用于评估效率分数，以用作预测中的替代工具。

设计/方法/方法

在这项研究中，提出了改进公理的提取 PPS 和用于评估效率得分的 BIP-DEA 模型。

发现

提出二进制整数模型以消除DEA中二进制值数据的挑战。

原创性/价值

所提出的模型对于包括医疗保健行业在内的许多领域的重要性在于它可以使用二进制数据预测事件的发生或不发生。该模型已被应用于评估中风疾病和机械障碍的最重要危险因素。该模型设定的目标可以帮助早期诊断疾病并增加患者康复的机会。