作者: Xiaoli Chu , Bingzhen Sun , Xue Li , Keyu Han , JiaQi Wu
DOI: 10.1016/J.INS.2020.05.039
关键词:
摘要: Using modern information theory to classify and identify high-risk disease groups is one of the research concerns in medical decision-making. The early diagnosis of gout is missing a single indicator, and relying on artificial labeling of disease characteristics is not only costly for decision-making, but also has a high misdiagnosis rate. Aiming at incomplete and attribute-related random large sample data, we propose a three-way clustering algorithm based on neighborhood rough sets, which is used to initially label the data, reduce the rate …