Notice of RetractionA sub-health risk appraisal model based on decision tree and rough sets

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.