机构：[1]Department of Control Science and Engineering, Tongji University, Shanghai 201804, China[2]The Department of Clinical Epidemiology and The Cardiovascular Medicine of Chinese Medical, Guang Dong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510120, China
Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in "treatment based on ZHENG differentiation." From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN-a multilabel learning model-is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance.
基金：
Natural Science Foundation
of China under grant nos. 61005006 and 61105053, as
well as the Fundamental Research Funds for the Central
Universities
第一作者机构：[1]Department of Control Science and Engineering, Tongji University, Shanghai 201804, China[2]The Department of Clinical Epidemiology and The Cardiovascular Medicine of Chinese Medical, Guang Dong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510120, China
通讯作者：
推荐引用方式(GB/T 7714)：
Li Guo-Zheng,Yan Shi-Xing,You Mingyu,et al.Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion[J].EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE.2012,2012:doi:10.1155/2012/837245.
APA：
Li, Guo-Zheng,Yan, Shi-Xing,You, Mingyu,Sun, Sheng&Ou, Aihua.(2012).Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion.EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE,2012,
MLA：
Li, Guo-Zheng,et al."Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion".EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2012.(2012)
