DocumentCode
1960076
Title
Notice of Retraction
A sub-health risk appraisal model based on decision tree and rough sets
Author
Xin Lu ; Licheng Liu
Author_Institution
Sch. of Software, Univ. of Electron. Sci. & Technol. of China (UESTC), Chengdu, China
Volume
7
fYear
2010
fDate
9-11 July 2010
Firstpage
464
Lastpage
467
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
There are some problems in people´s sub-health risk appraisal using current technology, for example, incomplete data, bias in the diagnosis and can not effectively predict participant´s the future health state. This paper presents a sub-health risk appraisal method based on data mining technique to resolve these issues. By introduction the rough sets preprocessing risk appraisal noise data, extraction of information entropy in the training set, combined with C4.5 decision tree algorithm, it established the sub-health risk appraisal prediction model. Experimental results confirm that this model than the normal method of decision tree model has higher prediction accuracy of sub-health state.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
There are some problems in people´s sub-health risk appraisal using current technology, for example, incomplete data, bias in the diagnosis and can not effectively predict participant´s the future health state. This paper presents a sub-health risk appraisal method based on data mining technique to resolve these issues. By introduction the rough sets preprocessing risk appraisal noise data, extraction of information entropy in the training set, combined with C4.5 decision tree algorithm, it established the sub-health risk appraisal prediction model. Experimental results confirm that this model than the normal method of decision tree model has higher prediction accuracy of sub-health state.
Keywords
data mining; decision trees; health care; risk analysis; rough set theory; data mining technique; decision tree algorithm; decision tree model; health state; information entropy; risk appraisal noise data; rough sets; subhealth risk appraisal model; subhealth risk appraisal prediction model; Appraisal; C4.5 algorithm; data preprocessing; rough sets; sub-health;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5565137
Filename
5565137
Link To Document