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

Medicine

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