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Article Type

Research Article

Abstract

Medical diagnosis is one of the most difficult fields in which decisions must be made due to the fact that medical information often has characteristics of uncertainty, incompleteness, imprecision and even contradiction. Traditional aggregation and decision-making methods are often not well suited to such complexities, and may result in less reliable diagnostic outcomes. In order to overcome these drawbacks, the authors propose a new approach using a novel representation of Interval-Valued Neutrosophic Sets (IVNSs), the Dombi operational laws, and Bonferroni Mean (BM) aggregation operators. The proposed framework is specifically aimed at coping with uncertainty, indeterminacy and falsity all at once with retaining the interrelationships of the decision attributes. Moreover, IVNS (Interval-Valued Neutrosophic Sets) improve this representation by assigning intervals as values instead of a specific value to each component, which allows to cover a broader range of uncertainty of real-world medical data. The main goal of the research is to present advanced aggregation methods that allow to assemble several uncertain evaluations into a sensible overall assessment, based on the properties of Interval-Valued Neutrosophic Numbers (IVNNs). Finally, in this study the developed operators are proved to be logically coherent, and they satisfy essential requirements for practical decision-making applications in the IVN environment, which leads to a new family of Dombi Bonferroni Mean aggregation operators.

Keywords

Dombi bonferroni mean, IVNWDBM, IVNWDGBM, Medical diagnosis, Sustainable energy

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