Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion
Joint Authors
Ou, Ai-hua
Yan, Shi-Xing
You, Mingyu
Sun, Sheng
Li, Guo-zheng
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-03
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Guo-zheng& Yan, Shi-Xing& You, Mingyu& Sun, Sheng& Ou, Ai-hua. 2012. Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion. Evidence-Based Complementary and Alternative Medicine،Vol. 2012, no. 2012, pp.1-5.
https://search.emarefa.net/detail/BIM-1028573
Modern Language Association (MLA)
Li, Guo-zheng…[et al.]. Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion. Evidence-Based Complementary and Alternative Medicine No. 2012 (2012), pp.1-5.
https://search.emarefa.net/detail/BIM-1028573
American Medical Association (AMA)
Li, Guo-zheng& Yan, Shi-Xing& You, Mingyu& Sun, Sheng& Ou, Ai-hua. Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion. Evidence-Based Complementary and Alternative Medicine. 2012. Vol. 2012, no. 2012, pp.1-5.
https://search.emarefa.net/detail/BIM-1028573
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1028573