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<p>Medical diagnosis involves identifying the type and cause of a disease, injury, or condition based onf( a patient's symptoms, medical history, test results, and examination findings. Accurate and timely diagnosis is crucial for effective patient treatment, clinical research, and healthcare policy, as it ensures that clinical decision-making (DM) is based on a precise assessment of the patient’s condition. Multi-attribute group decision-making (MAGDM) is a powerful tool for addressing real-world complexities; however, existing MAGDM methods often produce inconsistent or conflicting results. This study applies an MAGDM technique under the&#xa0;complex hesitant fuzzy soft set (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(CHFSS\)</EquationSource> </InlineEquation>)&#xa0;framework. The <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(CHF{S}_{\grave{f}t}S\)</EquationSource> </InlineEquation> is a novel mathematical model that integrates complex-valued membership grades, hesitant fuzzy elements, and parametrization tools. We first establish the <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(CHF{S}_{\grave{f}t}S\)</EquationSource> </InlineEquation> model by defining its fundamental operations, laws, and algebraic properties. Next, we propose a new algorithm based on&#xa0;complex hesitant fuzzy soft Numbers (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(CHF{S}_{\grave{f}t}S\)</EquationSource> </InlineEquation> Ns). Under the <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(CHF{S}_{\grave{f}t}S\)</EquationSource> </InlineEquation> framework, we develop an effective MAGDM approach to overcome the limitations of conventional MAGDM methods.To demonstrate its applicability, we employ the proposed MAGDM method in a medical diagnostic process to identify infections in patients. Compared to existing models, the extended <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(CHF{S}_{\grave{f}t}S\)</EquationSource> </InlineEquation> -based approach provides more accurate score values for assessing patients' health conditions, aiding medical professionals in determining disease severity. Furthermore, a comprehensive comparative analysis validates the practicality and superiority of the proposed technique over existing methods.</p>

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Extension of Vikor Method Based on Complex Hesitant Fuzzy Soft Set and Dombi Aggregation Operators to Medical Diagnostic Approach

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Rozia Gul,
  • Fazl Ghani,
  • Saleem Abdullah

摘要

Medical diagnosis involves identifying the type and cause of a disease, injury, or condition based onf( a patient's symptoms, medical history, test results, and examination findings. Accurate and timely diagnosis is crucial for effective patient treatment, clinical research, and healthcare policy, as it ensures that clinical decision-making (DM) is based on a precise assessment of the patient’s condition. Multi-attribute group decision-making (MAGDM) is a powerful tool for addressing real-world complexities; however, existing MAGDM methods often produce inconsistent or conflicting results. This study applies an MAGDM technique under the complex hesitant fuzzy soft set ( \(CHFSS\) ) framework. The \(CHF{S}_{\grave{f}t}S\) is a novel mathematical model that integrates complex-valued membership grades, hesitant fuzzy elements, and parametrization tools. We first establish the \(CHF{S}_{\grave{f}t}S\) model by defining its fundamental operations, laws, and algebraic properties. Next, we propose a new algorithm based on complex hesitant fuzzy soft Numbers ( \(CHF{S}_{\grave{f}t}S\) Ns). Under the \(CHF{S}_{\grave{f}t}S\) framework, we develop an effective MAGDM approach to overcome the limitations of conventional MAGDM methods.To demonstrate its applicability, we employ the proposed MAGDM method in a medical diagnostic process to identify infections in patients. Compared to existing models, the extended \(CHF{S}_{\grave{f}t}S\) -based approach provides more accurate score values for assessing patients' health conditions, aiding medical professionals in determining disease severity. Furthermore, a comprehensive comparative analysis validates the practicality and superiority of the proposed technique over existing methods.