Cognitive Analysis of Medical Decision-Making: An Extended MULTIMOORA-Based Multigranulation Probabilistic Model with Evidential Reasoning

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wenhui Bai, Chao Zhang, Yanhui Zhai, Arun Kumar Sangaiah, Baoli Wang, Wentao Li
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Abstract

Cognitive computation has leveraged the capabilities of computer algorithms, rendering it an exceptionally efficient approach for addressing multi-attribute group decision-making (MAGDM) problems. Due to the stability of MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and the capability of evidential reasoning (ER) to combine information from multiple sources, the technique of multigranulation probabilistic rough sets (MG PRSs) holds great promise for solving MAGDM problems. Thus, a new and stable method for MAGDM is proposed. Initially, three forms of multigranulation Pythagorean fuzzy probabilistic rough sets (MG PF PRSs) are constructed using MULTIMOORA approaches. Next, the hierarchical clustering method is employed to cluster similar decision information and consolidate the decision-makers’ preferences. Representatives are chosen from each category to simplify information fusion calculations and reduce complexity by reducing the model’s dimensionality. Following that, the rankings obtained from the three methods are fused using ER. Ultimately, the validity of our method is revealed via a case analysis on chickenpox cases from the UCI data set by employing cognitive analysis. The paper outlines a method for MAGDM that provides significant advantages. Specifically, the use of MULTIMOORA improves the stability of decision results, while the incorporation of ER reduces the overall uncertainty of entire decision processes.

Abstract Image

医疗决策的认知分析:基于 MULTIMOORA 的多粒度概率模型与证据推理的扩展模型
认知计算充分利用了计算机算法的能力,使其成为解决多属性群体决策(MAGDM)问题的一种异常有效的方法。由于 MULTIMOORA(比率分析法多目标优化加全 MULTIplicative 形式)的稳定性和证据推理(ER)结合多种来源信息的能力,多概率粗糙集(MG PRSs)技术在解决 MAGDM 问题上大有可为。因此,本文提出了一种新的、稳定的 MAGDM 方法。首先,使用 MULTIMOORA 方法构建了三种形式的多粒度毕达哥拉斯模糊概率粗糙集(MG PF PRSs)。然后,采用分层聚类方法对相似的决策信息进行聚类,并整合决策者的偏好。从每个类别中选择代表,以简化信息融合计算,并通过降低模型的维度来降低复杂性。然后,使用 ER 融合三种方法得到的排名。最后,通过对 UCI 数据集中的水痘病例进行认知分析,揭示了我们方法的有效性。本文概述了一种具有显著优势的 MAGDM 方法。具体来说,MULTIMOORA 的使用提高了决策结果的稳定性,而 ER 的加入则降低了整个决策过程的整体不确定性。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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