Problem Hepatitis is a globally significant hepatic disorder characterized by inflammation of the liver and high mortality rates. The substantial overlap in clinical symptoms among its various forms often leads to diagnostic ambiguity, making it difficult to determine the specific type and severity of infection. These diagnostic challenges necessitate a mathematically grounded decision-making framework capable of managing uncertainty and interrelated medical parameters with precision. Aim This study aims to extend the existing HyFHSS paradigm to a more generalized structure, termed the Ç-HyFHSS. The primary objective is to establish a comprehensive theoretical foundation that enhances the representation and processing of Ç-HyFHS information in complex decision-making scenarios. Methods To achieve this objective, novel operational laws governing Ç-HyFHSNs are developed to formalize their algebraic characteristics and computational behavior. Based on these formulations, two advanced aggregation mechanisms the Ç-HyFHSWA and Ç-HyFHSWG operators are introduced to facilitate multi-criteria information fusion under uncertain conditions. A systematic decision-making algorithm incorporating these operators is proposed, allowing for unequal decision-maker weights. Theoretical properties such as boundedness, monotonicity, and idempotency are rigorously examined to ensure mathematical soundness and logical coherence. Results A practical case study involving the classification of hepatitis types is conducted to demonstrate the applicability and computational efficiency of the proposed method. Comparative analyses with existing FHSS frameworks indicate that the Ç-HyFHSS-based approach exhibits superior performance in terms of accuracy, adaptability, and robustness when handling complex and uncertain information. Conclusion The proposed Ç-HyFHSS framework represents a significant advancement in FHSS theory by integrating cC-HyFS structures with newly defined aggregation operators. This integration strengthens the mathematical expressiveness and analytical flexibility of fuzzy decision models, offering a robust and efficient tool for complex multi-criteria decision-making applications, particularly in diagnostic and uncertainty-driven environments.

Advancing hepatitis diagnosis with hyperbolic fuzzy hypersoft sets in MCDM applications

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发表日期：

2026年1月4日

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Pairote Yiarayong

摘要

Problem

Hepatitis is a globally significant hepatic disorder characterized by inflammation of the liver and high mortality rates. The substantial overlap in clinical symptoms among its various forms often leads to diagnostic ambiguity, making it difficult to determine the specific type and severity of infection. These diagnostic challenges necessitate a mathematically grounded decision-making framework capable of managing uncertainty and interrelated medical parameters with precision.

Aim

This study aims to extend the existing HyFHSS paradigm to a more generalized structure, termed the Ç-HyFHSS. The primary objective is to establish a comprehensive theoretical foundation that enhances the representation and processing of Ç-HyFHS information in complex decision-making scenarios.

Methods

To achieve this objective, novel operational laws governing Ç-HyFHSNs are developed to formalize their algebraic characteristics and computational behavior. Based on these formulations, two advanced aggregation mechanisms the Ç-HyFHSWA and Ç-HyFHSWG operators are introduced to facilitate multi-criteria information fusion under uncertain conditions. A systematic decision-making algorithm incorporating these operators is proposed, allowing for unequal decision-maker weights. Theoretical properties such as boundedness, monotonicity, and idempotency are rigorously examined to ensure mathematical soundness and logical coherence.

Results

A practical case study involving the classification of hepatitis types is conducted to demonstrate the applicability and computational efficiency of the proposed method. Comparative analyses with existing FHSS frameworks indicate that the Ç-HyFHSS-based approach exhibits superior performance in terms of accuracy, adaptability, and robustness when handling complex and uncertain information.

Conclusion

The proposed Ç-HyFHSS framework represents a significant advancement in FHSS theory by integrating cC-HyFS structures with newly defined aggregation operators. This integration strengthens the mathematical expressiveness and analytical flexibility of fuzzy decision models, offering a robust and efficient tool for complex multi-criteria decision-making applications, particularly in diagnostic and uncertainty-driven environments.