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<p>Medical diagnosis often involves uncertain and heterogeneous information, requiring effective decision-support tools. To address this challenge, this study introduces the disc fractional fuzzy set (ĎFFS), a novel framework that combines fractional parameterization with circular representations of membership and non-membership degrees. Based on this framework, new operational laws and weighted aggregation operators are developed, and their fundamental properties are established. A diagnostic decision-making algorithm is subsequently formulated using the proposed operators and ranking. A COVID-19 diagnostic case study demonstrates the applicability of the approach. Numerical and comparative analyses indicate that the ĎFFS framework provides reliable decision outcomes, enhanced representational capability, and improved information aggregation performance compared with existing fuzzy models.</p>

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A Diagnostic Decision-Making Framework Based on Disc Fractional Fuzzy Sets and Novel Aggregation Operators

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Pairote Yiarayong

摘要

Medical diagnosis often involves uncertain and heterogeneous information, requiring effective decision-support tools. To address this challenge, this study introduces the disc fractional fuzzy set (ĎFFS), a novel framework that combines fractional parameterization with circular representations of membership and non-membership degrees. Based on this framework, new operational laws and weighted aggregation operators are developed, and their fundamental properties are established. A diagnostic decision-making algorithm is subsequently formulated using the proposed operators and ranking. A COVID-19 diagnostic case study demonstrates the applicability of the approach. Numerical and comparative analyses indicate that the ĎFFS framework provides reliable decision outcomes, enhanced representational capability, and improved information aggregation performance compared with existing fuzzy models.