Determination of disease risk factors using binary data envelopment analysis and logistic regression analysis (case study: a stroke risk factors)

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-10-16 DOI:10.1108/jm2-09-2022-0224

Maedeh Gholamazad, Jafar Pourmahmoud, Alireza Atashi, Mehdi Farhoudi, Reza Deljavan Anvari

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引用次数: 0

Abstract

Purpose A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran. Design/methodology/approach In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted. Findings The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed. Originality/value The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

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使用二元数据包络分析和逻辑回归分析确定疾病危险因素(案例研究:卒中危险因素)

中风是一种严重的、危及生命的疾病，当大脑的一部分血液供应被切断时就会发生。中风越早得到治疗，可能发生的损害就越小。及时准确的预测和诊断是加快治疗的方法之一。本文旨在比较二进制整数规划-数据包络分析(BIP-DEA)模型和逻辑回归(LR)模型对伊朗脑卒中的诊断和预测。设计/方法/方法本研究引入了BIP-DEA和LR两种算法，提取了导致脑卒中的关键危险因素。研究人群包括2100个样本(患者)，分为6个不同大小的亚样本。从各算法的分类表可以看出，在数据量较小的情况下，BIP-DEA模型的结果比LR更可靠。在运行每个算法后，BIP-DEA和LR算法分别确定了8个和5个更有效的中风危险因素和原因。最后，提出了基于重要风险因素的预测模型。本研究的主要目的是提供集成的BIP-DEA算法作为一种快速、简便、适用的评估和预测工具。事实上，在样本量较小的情况下，BIP-DEA算法可以作为LR模型的替代工具。这些算法可用于包括医疗保健行业在内的各个领域，在患者病情变得更危险之前预测和预防各种疾病。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.