Community Partition Model of Patients with Heterogeneous Attributes Based on Composite Rough Sets
LIU Chenxi1, SUN Bingzhen1, CHU Xiaoli2, QI Chang1,3
1. School of Economics and Management, Xidian University, Xi’an 710071, China; 2. State Key Laboratory of Dampness Syndrome of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, China; 3. College Students Mental Health Education Service Center, Lanzhou Jiaotong University, Lanzhou 730070, China
Abstract：Community partition is an important part of network research. Community partition of patients with Rheumatoid Arthritis based on medical data can effectively improve the accuracy of clinical medical decision-making. Considering that there may be problems of heterogeneity and correlation of patients′ attributes in the process of community partition, this paper firstly classifies patients based on composite rough sets theory to effectively deal with heterogeneous attributes. Secondly, the rough sets theory and the louvain algorithm are combined to build a community partition model of patients with heterogeneous attributes. By using the clinical real dataset and the classical network dataset, it is verified that the proposed model can obtain the community structure with large module value, and the realize the division of patients with different disease activity levels into different communities, so as to improve the effectiveness and accuracy of the assessment of patients′ disease activity level.
刘晨曦, 孙秉珍, 楚晓丽, 祁畅. 基于复合粗糙集的异构属性患者社区划分模型[J]. 复杂系统与复杂性科学, 2023, 20(3): 27-34.
LIU Chenxi, SUN Bingzhen, CHU Xiaoli, QI Chang. Community Partition Model of Patients with Heterogeneous Attributes Based on Composite Rough Sets. Complex Systems and Complexity Science, 2023, 20(3): 27-34.
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