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<p>The growing demand for intelligent decision-making support systems highlights the importance of methodologies that improve decision-making in the context of uncertainty. To address decision-making with uncertainties, this paper presents a novel approach of decision-making under uncertainty by introducing the Fermatean probabilistic hesitant fuzzy rough weighted averaging/geometric aggregation operators. The suggested decision-making procedure not only extends the averaging aggregation operators and its applications but also integrates probabilistic information with Fermatean hesitant fuzzy rough sets to enrich the existing notion and implementation. Moreover, it establishes a method for evaluating and making decisions regarding digitalisation solutions within medical systems, based in the application of the suggested operators. Perform a comprehensive analysis of reliability and validity assessments to demonstrate the efficacy of the suggested methodology for achieving reliable outcomes. On the other hand, this research thoroughly conducts the comparative analysis utilising the Fermatean probabilistic hesitant fuzzy rough TOPSIS methodology with the newly introduced approach. This highlights the reliability and authenticity of the innovative technique and emphasises its practical characteristics. The findings indicate that the methodology formulated in the present research effectively expresses fuzzy evaluation information within challenging situations, providing insights for system designers to enhance decision-making processes concerning evaluating and making smart decisions of intelligent medical solutions. This approach overcomes the limitations of traditional ranking aggregation methods, providing a more reliable understanding of the stability and reliability of recommendations. By offering a more adaptive and uncertainty-resilient framework, this study advances multi-criteria decision analysis and improves reliability of recommendations provided by decision support systems. The Fermatean probabilistic hesitant fuzzy rough sets provide us greater accuracy but at the cost of time consumption, which is the limitation.</p>

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Multi-criteria Decision Support System for the Evaluation of Digitalization in Medical Systems Based on Fermatean Probabilistic Hesitant Fuzzy Rough Information

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Yasser Alrashedi,
  • Theyab Alrashdi,
  • Attaullah

摘要

The growing demand for intelligent decision-making support systems highlights the importance of methodologies that improve decision-making in the context of uncertainty. To address decision-making with uncertainties, this paper presents a novel approach of decision-making under uncertainty by introducing the Fermatean probabilistic hesitant fuzzy rough weighted averaging/geometric aggregation operators. The suggested decision-making procedure not only extends the averaging aggregation operators and its applications but also integrates probabilistic information with Fermatean hesitant fuzzy rough sets to enrich the existing notion and implementation. Moreover, it establishes a method for evaluating and making decisions regarding digitalisation solutions within medical systems, based in the application of the suggested operators. Perform a comprehensive analysis of reliability and validity assessments to demonstrate the efficacy of the suggested methodology for achieving reliable outcomes. On the other hand, this research thoroughly conducts the comparative analysis utilising the Fermatean probabilistic hesitant fuzzy rough TOPSIS methodology with the newly introduced approach. This highlights the reliability and authenticity of the innovative technique and emphasises its practical characteristics. The findings indicate that the methodology formulated in the present research effectively expresses fuzzy evaluation information within challenging situations, providing insights for system designers to enhance decision-making processes concerning evaluating and making smart decisions of intelligent medical solutions. This approach overcomes the limitations of traditional ranking aggregation methods, providing a more reliable understanding of the stability and reliability of recommendations. By offering a more adaptive and uncertainty-resilient framework, this study advances multi-criteria decision analysis and improves reliability of recommendations provided by decision support systems. The Fermatean probabilistic hesitant fuzzy rough sets provide us greater accuracy but at the cost of time consumption, which is the limitation.