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<p>The combined diagnosis data of Traditional Chinese and Western medicine is characterized by intricate structures and high dimensionality. These complexities present significant challenges for clinical decision-making. Community partitioning offers a powerful approach to uncover hidden relationships within such medical data. It provides theoretical support for discovering potential comorbidities and formulating personalized treatment plans. To address attribute redundancy in medical datasets, this study proposes a novel community partitioning and node prediction framework. First, we employ a Weighted Neighborhood Rough Set algorithm to eliminate redundant features and screen for key diagnostic attributes. Second, based on the reduced attributes, we construct a patient similarity network and utilize the Louvain algorithm for initial community partitioning. Third, to enhance precision, Fuzzy Rough Set theory is introduced to handle boundary nodes. By defining the distance between nodes and communities, this step resolves the ambiguity of edge node classification. Finally, we incorporate the PageRank algorithm to predict the community attribution of new patients. The proposed method is applied to the field of Chronic Kidney Disease treatment. Experimental results on real clinical data demonstrate the scientific rigor and superior performance of the proposed framework compared to traditional methods.</p>

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Community partitioning and node belonging prediction method based on neighborhood attribute reduction and fuzzy rough set with application in clinical decision making

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Haoran Sun,
  • Xiangtang Chen,
  • Bingzhen Sun,
  • Xixuan Zhao,
  • Xiaoli Chu,
  • Jianxiong Cai

摘要

The combined diagnosis data of Traditional Chinese and Western medicine is characterized by intricate structures and high dimensionality. These complexities present significant challenges for clinical decision-making. Community partitioning offers a powerful approach to uncover hidden relationships within such medical data. It provides theoretical support for discovering potential comorbidities and formulating personalized treatment plans. To address attribute redundancy in medical datasets, this study proposes a novel community partitioning and node prediction framework. First, we employ a Weighted Neighborhood Rough Set algorithm to eliminate redundant features and screen for key diagnostic attributes. Second, based on the reduced attributes, we construct a patient similarity network and utilize the Louvain algorithm for initial community partitioning. Third, to enhance precision, Fuzzy Rough Set theory is introduced to handle boundary nodes. By defining the distance between nodes and communities, this step resolves the ambiguity of edge node classification. Finally, we incorporate the PageRank algorithm to predict the community attribution of new patients. The proposed method is applied to the field of Chronic Kidney Disease treatment. Experimental results on real clinical data demonstrate the scientific rigor and superior performance of the proposed framework compared to traditional methods.