DocumentCode
501256
Title
Notice of Retraction
A System to Assist Medical Diagnosis Based on Rough Sets
Author
Nie Bin ; Wang Mingyan ; Qiu Taorong ; Liu Qing ; Fang Na
Author_Institution
Dept. of Comput. Sci., Nanchang Univ., Nanchang, China
Volume
2
fYear
2009
fDate
15-17 May 2009
Firstpage
563
Lastpage
566
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.
Without a correct diagnosis, a correct treatment may not be made in medical science. The false negative or the false positive, and other factors usual appear in medical diagnosis. Rule induction methods generate rules and match model whose general methods are difficult to assist medical diagnosis. In the paper, some concepts such as rough inclusive, rough exclusive, rough contrast, tolerant rough sets and so on are introduced, and a system to assist medical diagnosis based on rough sets (AMDSRS), based on these concepts, is put forward. It was proved to be feasible and effective after tested with a database, and a good proposal, which could assist medical diagnosis in accordance with diagnostic needs.
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.
Without a correct diagnosis, a correct treatment may not be made in medical science. The false negative or the false positive, and other factors usual appear in medical diagnosis. Rule induction methods generate rules and match model whose general methods are difficult to assist medical diagnosis. In the paper, some concepts such as rough inclusive, rough exclusive, rough contrast, tolerant rough sets and so on are introduced, and a system to assist medical diagnosis based on rough sets (AMDSRS), based on these concepts, is put forward. It was proved to be feasible and effective after tested with a database, and a good proposal, which could assist medical diagnosis in accordance with diagnostic needs.
Keywords
knowledge acquisition; medical diagnostic computing; patient diagnosis; rough set theory; match model; medical diagnosis; medical science; rough contrast; rough exclusive; rough inclusive; rule generation; rule induction method; tolerant rough sets; Application software; Computer science; Databases; Diabetes; Information systems; Information technology; Medical diagnosis; Medical diagnostic imaging; Proposals; Rough sets; diagnosis system; rough contrast; rough exclusive; rough inclusive; torlant rough;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Technology and Applications, 2009. IFITA '09. International Forum on
Conference_Location
Chengdu
Print_ISBN
978-0-7695-3600-2
Type
conf
DOI
10.1109/IFITA.2009.176
Filename
5231408
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