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Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine

Authors :
Yong-Zhi Li
Guo-Zheng Li
Jian-Yi Gao
Zhi-Feng Zhang
Quan-Chun Fan
Jia-Tuo Xu
Gui-E Bai
Kai-Xian Chen
Hong-Zhi Shi
Sheng Sun
Yu Liu
Feng-Feng Shao
Tao Mi
Xin-Hong Jia
Shuang Zhao
Jia-Chang Chen
Jun-Lian Liu
Yu-Meng Guo
Li Ping Tu
Source :
The Scientific World Journal, Vol 2015 (2015)
Publication Year :
2015
Publisher :
Hindawi Limited, 2015.

Abstract

Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differentiation. We adopt statistics method to describe the syndrome factor regular pattern of spaceman volunteers. Hybrid optimization based multilabel (HOML) is used as feature selection method and multilabel k-nearest neighbors (ML-KNN) is applied. According to the syndrome factor statistical result, we find that qi deficiency is a base syndrome pattern throughout the entire experiment process and, at the same time, there are different associated syndromes such as liver depression, spleen deficiency, dampness stagnancy, and yin deficiency, due to differences of individual situation. With feature selection, we screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multilabel classification model reaches 80%.

Subjects

Subjects :
Technology
Medicine
Science

Details

Language :
English
ISSN :
23566140 and 1537744X
Volume :
2015
Database :
Directory of Open Access Journals
Journal :
The Scientific World Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.6481837039d40eba122eb2356cd26ad
Document Type :
article
Full Text :
https://doi.org/10.1155/2015/125736