Ensemble learning for synthesis of the four diagnostics of TCM

Na Chu; Lizhuang Ma; Xiaoyu Chen; Zhiying Che; Yiyang Hu

doi:10.1109/BIBMW.2011.6112483

Ensemble learning for synthesis of the four diagnostics of TCM

Na Chu^*
, Lizhuang Ma
, Xiaoyu Chen
, Zhiying Che
, Yiyang Hu

^*Corresponding author for this work

Shanghai Jiao Tong University
Shanghai University of Traditional Chinese Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This paper outlines the procedure of synthesis of the four diagnostics of traditional Chinese medicine (TCM). It is an important part of the modernization of TCM diagnosis. We apply the principle of ensemble learning, and present a systematic framework for synthesis of four diagnostic. Especially the logistic regression and LogitBoost methods are introduced. Experiment results on chronic hepatitis B dataset demonstrate that the proposed framework is suitable to the application of TCM diagnosis in clinical, and able to obtain a smaller and satisfactory critical feature subset, 15 critical features of TCM are selected from original 123 features. The critical features are in sound agreement with those used by the physicians in making their clinical decisions. At the same time, we obtain better performance in discriminating the syndromes of CHB. The classification accuracy is 95.3153%.

Original language	English
Title of host publication	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Pages	843-847
Number of pages	5
DOIs	https://doi.org/10.1109/BIBMW.2011.6112483
State	Published - 2011
Externally published	Yes
Event	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 - Atlanta, GA, United States Duration: 12 Nov 2011 → 15 Nov 2011

Publication series

Name	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Conference

Conference	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Country/Territory	United States
City	Atlanta, GA
Period	12/11/11 → 15/11/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

ensemble learning
synthesis of four diagnostics
traditional Chinese medicine

Access to Document

10.1109/BIBMW.2011.6112483

Cite this

Chu, N., Ma, L., Chen, X., Che, Z., & Hu, Y. (2011). Ensemble learning for synthesis of the four diagnostics of TCM. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 (pp. 843-847). Article 6112483 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011). https://doi.org/10.1109/BIBMW.2011.6112483

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title = "Ensemble learning for synthesis of the four diagnostics of TCM",

abstract = "This paper outlines the procedure of synthesis of the four diagnostics of traditional Chinese medicine (TCM). It is an important part of the modernization of TCM diagnosis. We apply the principle of ensemble learning, and present a systematic framework for synthesis of four diagnostic. Especially the logistic regression and LogitBoost methods are introduced. Experiment results on chronic hepatitis B dataset demonstrate that the proposed framework is suitable to the application of TCM diagnosis in clinical, and able to obtain a smaller and satisfactory critical feature subset, 15 critical features of TCM are selected from original 123 features. The critical features are in sound agreement with those used by the physicians in making their clinical decisions. At the same time, we obtain better performance in discriminating the syndromes of CHB. The classification accuracy is 95.3153\%.",

keywords = "ensemble learning, synthesis of four diagnostics, traditional Chinese medicine",

author = "Na Chu and Lizhuang Ma and Xiaoyu Chen and Zhiying Che and Yiyang Hu",

year = "2011",

doi = "10.1109/BIBMW.2011.6112483",

language = "英语",

isbn = "9781457716133",

series = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

pages = "843--847",

booktitle = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

note = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 ; Conference date: 12-11-2011 Through 15-11-2011",

}

Chu, N, Ma, L, Chen, X, Che, Z & Hu, Y 2011, Ensemble learning for synthesis of the four diagnostics of TCM. in 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011., 6112483, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, pp. 843-847, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, Atlanta, GA, United States, 12/11/11. https://doi.org/10.1109/BIBMW.2011.6112483

Ensemble learning for synthesis of the four diagnostics of TCM. / Chu, Na; Ma, Lizhuang; Chen, Xiaoyu et al.
2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. p. 843-847 6112483 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Chu, Na

AU - Ma, Lizhuang

AU - Chen, Xiaoyu

AU - Che, Zhiying

AU - Hu, Yiyang

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AB - This paper outlines the procedure of synthesis of the four diagnostics of traditional Chinese medicine (TCM). It is an important part of the modernization of TCM diagnosis. We apply the principle of ensemble learning, and present a systematic framework for synthesis of four diagnostic. Especially the logistic regression and LogitBoost methods are introduced. Experiment results on chronic hepatitis B dataset demonstrate that the proposed framework is suitable to the application of TCM diagnosis in clinical, and able to obtain a smaller and satisfactory critical feature subset, 15 critical features of TCM are selected from original 123 features. The critical features are in sound agreement with those used by the physicians in making their clinical decisions. At the same time, we obtain better performance in discriminating the syndromes of CHB. The classification accuracy is 95.3153%.

KW - ensemble learning

KW - synthesis of four diagnostics

KW - traditional Chinese medicine

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M3 - 会议稿件

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BT - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

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Y2 - 12 November 2011 through 15 November 2011

ER -

Ensemble learning for synthesis of the four diagnostics of TCM

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Keywords

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