Citation

Nagarajan, Deivanayagampillai and Thangavel, Bhuvaneswari and Suppiah, Yasothei and Anbalagan, Kanchana (2026) Multi-attribute decision-making in hesitant fuzzy environments. In: Data-driven Decision Making and Soft Computing. CRC Press, pp. 207-234. ISBN 978-100363483-6, 978-104106297-4, 978-104106314-8

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Abstract

Early-stage detection of Alzheimer’s disease (AD) can be feasible through the term Mild Cognitive Impairment (MCI), a neurodegenerative disorder that affects brain cells, which is essential to prevent the disease progression and minimize further cell loss through additional medical treatment. This study analyses the application of fuzzy set theory in the early stage of AD detection by employing concepts like mean value, scoring function, and variance within the environment of generalized hesitant fuzzy numbers. This chapter introduces definitions for operators like generalized hesitant fuzzy ordered weighted, geometric, and their combinations. It further applies the obtained results to address uncertain multi-attribute group decision-making (MAGDM) problems by illustrating using a characteristic of AD. First, the issue is evaluated using quantitative assessment findings and demonstrates how the qualities in hesitant weight geometric aggregation (HFWGA) and hesitant weight average aggregation (HFWAA) remain in the same order, with minor differences in D3 and D4 according to the TOmada de Decisão Iterativa Multicritério (TODIM) technique. When compared to TODIM, HFWAA and HFWGA prove to be more effective decision-making techniques. The novelty of the work lies in its comprehensive approach to addressing uncertainty, ambiguity, and group decision dynamics within decision-making frameworks, offering valuable insights and potential advancements in the generalized hesitant fuzzy. The validity of the proposed methods is assessed through comparison and testing criteria. The advantage of the proposed method is flexibility in modelling uncertainty, and challenges persist in handling hazy and ambiguous data.

Item Type:	Book Section
Uncontrolled Keywords:	Alzheimer, Brain cells
Subjects:	R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 06:33
Last Modified:	04 Sep 2026 06:33
URII:	http://shdl.mmu.edu.my/id/eprint/16723

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