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<p>Multi-Criteria Decision-Making (MCDM) methods, and in particular their fuzzy extensions, have become indispensable tools for analyzing complex strategic problems where uncertainty plays a central role. Yet the conventional Type-1 fuzzy framework often proves inadequate for capturing the deeper layers of indeterminacy inherent in real-world decisions. Type-2 fuzzy sets provide a powerful means of capturing higher-order uncertainty, while the symmetric form of Gaussian functions offers a balanced and realistic way to represent ambiguity in practical problems, and Pythagorean fuzzy logic adds the unique capacity to model both truth and falsity; yet their combined strengths remain scarcely utilized in existing research. This study advances the field by introducing Finite Interval Type-2 Gaussian Pythagorean Fuzzy Numbers (FIT2GPFNs), a construct that expands the descriptive capacity of fuzzy theory while retaining computational feasibility. The framework is reinforced through the formulation of arithmetic operators, a value–ambiguity based ranking method, and a signed distance function specifically designed for FIT2GPFNs. These foundations are then applied within an extended DEMATEL methodology to the case of early diabetes detection. The proposed method not only separates causal and effect criteria but also ranks them within their respective groups, thereby identifying which criteria should be prioritized in diagnosis. Comparative analysis against alternative MCDM techniques further underscores the robustness and practical relevance of the approach.</p>

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Enhancing Diabetes Diagnosis Using FIT2GPFN-Integrated DEMATEL Method: A Multi-Criteria Fuzzy Decision-Making Approach

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Sayon Bakshi,
  • Mahatab Uddin Molla,
  • Bibhas C. Giri

摘要

Multi-Criteria Decision-Making (MCDM) methods, and in particular their fuzzy extensions, have become indispensable tools for analyzing complex strategic problems where uncertainty plays a central role. Yet the conventional Type-1 fuzzy framework often proves inadequate for capturing the deeper layers of indeterminacy inherent in real-world decisions. Type-2 fuzzy sets provide a powerful means of capturing higher-order uncertainty, while the symmetric form of Gaussian functions offers a balanced and realistic way to represent ambiguity in practical problems, and Pythagorean fuzzy logic adds the unique capacity to model both truth and falsity; yet their combined strengths remain scarcely utilized in existing research. This study advances the field by introducing Finite Interval Type-2 Gaussian Pythagorean Fuzzy Numbers (FIT2GPFNs), a construct that expands the descriptive capacity of fuzzy theory while retaining computational feasibility. The framework is reinforced through the formulation of arithmetic operators, a value–ambiguity based ranking method, and a signed distance function specifically designed for FIT2GPFNs. These foundations are then applied within an extended DEMATEL methodology to the case of early diabetes detection. The proposed method not only separates causal and effect criteria but also ranks them within their respective groups, thereby identifying which criteria should be prioritized in diagnosis. Comparative analysis against alternative MCDM techniques further underscores the robustness and practical relevance of the approach.