基于灰色聚类模型的老年人能力评价新方法

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-09-22 DOI:10.1108/jm2-09-2022-0215

Weiliang Zhang, Sifeng Liu, Junliang Du, Liangyan Tao, Wenjie Dong

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引用次数: 0

摘要

目的提出一种新的老年人能力评价指标体系和评价方法。设计/方法/方法本研究构建了包含4个一级指标、17个二级指标的老年人能力综合评价指标体系。将灰色聚类分析与熵权法相结合，建立了一个稳健的老年人能力评价模型。结果表明，所建立的灰色聚类模型能够较好地计算老年人的残疾水平。构建的指标体系更全面地考虑了老年人残疾的各个方面。本研究为确定老年人残疾水平提供了一种定量的方法和更为合理的指标体系。

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A novel evaluation approach for the ability of older adults based on grey clustering model

Purpose The purpose of this study is to advance a novel evaluation index system and evaluation approach for ability of older adults in China. Design/methodology/approach This study constructed a comprehensive older adult ability evaluation index system with 4 primary indicators and 17 secondary indicators. Grey clustering analysis and entropy weight method are combined into a robust evaluation model for the ability of older adults. Findings The result demonstrates that the proposed grey clustering model is readily available to calculate the disability level of elderly individuals. The constructed index system more comprehensively considers all aspects of the disability of the elderly. Originality/value This study provides a quantitative method and a more reasonable index system for the determination of the disability level of the elderly.

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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.