三角模糊不完备三支群决策及其在糖尿病诊断中的应用

Triangular Fuzzy Incomplete Three-Way Group Decision-Making and Its Application in Diabetes Diagnosis

为同时应对不确定信息表示与风险信息融合对群决策带来的挑战,构建一种三角模糊不完备三支群决策方法,并将其应用于糖尿病诊断决策。首先,针对信息不确定性蕴含的模糊性和不完备性,分别引入三角模糊集和不完备信息系统的概念。通过与多粒度三支决策结合,构建了可调多粒度三角模糊概率粗糙集模型。然后,根据离差最大化法计算属性权重与专家权重,结合ELECTRE(elimination et choice translating reality)方法建立了三角模糊多属性群决策方法。最后,通过对糖尿病患者数据的案例分析和评估,验证了所提方法的可行性和有效性。该方法不仅从不确定信息表示、风险信息融合和最优粒度选择的视角丰富了多粒度三支群决策理论,而且推动了糖尿病智能诊断方面的应用。

This paper constructs a triangular fuzzy incomplete three-way group decision-making method to simultaneously address the challenges posed by uncertain information representation and risk information fusion in group decision-making,and applies this method to diabetes diagnosis.First,the concepts of triangular fuzzy sets and incomplete information systems are introduced to address the fuzziness and incompleteness associated with information uncertainty.By integrating these concepts with multi-granularity three-way decision-making,an adjustable multi-granularity triangular fuzzy probabilistic rough set model is developed.Subsequently,using the maximum deviation method,attribute weights and expert weights are calculated,and combined with the ELECTRE(elimination et choice translating reality)method to establish a triangular fuzzy multi-attribute group decision-making method.Finally,the feasibility and effectiveness of the proposed method are validated through case analysis and evaluation of diabetic patient data.This method not only enriches the theory of multi-granularity three-way group decision-making from the perspectives of uncertain information representation,risk information fusion,and optimal granularity selection,but also advances its application in intelligent diabetes diagnosis.