A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment

He Li*, Mohammad Yazdi, Hong-Zhong Huang, Cheng-Geng Huang, Weiwen Peng, Arman Nedjati, Kehinde A. Adesina

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)
39 Downloads (Pure)

Abstract

Healthcare tends to be one of the most complicated sectors, and hospitals exist at the core of healthcare activities. One of the most significant elements in hospitals is service quality level. Moreover, the dependency between factors, dynamic features, as well as objective and subjective uncertainties involved endure challenges to modern decision-making problems. Thus, in this paper, a decision-making approach is developed for hospital service quality assessment, using a Bayesian copula network based on a fuzzy rough set within neighborhood operators as a basis of that to deal with dynamic features as well as objective uncertainties. In the copula Bayesian network model, the Bayesian Network is utilized to illustrate the interrelationships between different factors graphically, while Copula is engaged in obtaining the joint probability distribution. Fuzzy rough set theory within neighborhood operators is employed for the subjective treatment of evidence from decision makers. The efficiency and practicality of the designed method are validated by an analysis of real hospital service quality in Iran. A novel framework for ranking a group of alternatives with consideration of different criteria is proposed by the combination of the Copula Bayesian Network and the extended fuzzy rough set technique. The subjective uncertainty of decision makers’ opinions is dealt with in a novel extension of fuzzy Rough set theory. The results highlighted that the proposed method has merits in reducing uncertainty and assessing the dependency between factors of complicated decision-making problems.

Original languageEnglish
Pages (from-to)5527-5553
Number of pages27
JournalComplex and Intelligent Systems
Volume9
Issue number5
Early online date24 Mar 2023
DOIs
Publication statusPublished - Oct 2023
Externally publishedYes

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • MCDM
  • Bayesian analysis
  • Fuzzy set theory
  • Hospital service quality
  • Operation management

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