A Knowledge Acquisition Model of Inconsistent Medical Data Based on Rough Sets Theory

Author

Hua, Jiang

Author_Institution

Sch. of Econ. & Manage., Hebei Univ. of Eng., Handan

Volume

1

fYear

2008

fDate

20-22 Dec. 2008

Firstpage

176

Lastpage

180

Abstract

Knowledge acquisition includes the elicitation, collection, analysis, modeling and validation of knowledge for knowledge engineering and knowledge management projects. It is very valuable and has great development prospects to apply various knowledge acquisition techniques in medical data to explore the interrelations and laws between various diseases, sum up the medical effects of various treatment schemes and carry on diagnosis, treatment and medical research. However, the incomplete and inconsistent medical data make many knowledge acquisition methods ineffective. Rough sets theory is a mathematical tool for extracting knowledge from uncertain and incomplete information. The paper tries to apply the reduction of rough sets to remove redundant medical data and access to real and effective medical knowledge for finding the rules and models of medical diagnosis.

Keywords

data reduction; diseases; knowledge acquisition; medical computing; medical diagnostic computing; patient diagnosis; patient treatment; rough set theory; disease; inconsistent medical data; knowledge acquisition; knowledge engineering; knowledge extraction; knowledge management; mathematical tool; medical diagnosis treatment; rough set theory; Biomedical imaging; Data mining; Diseases; Knowledge acquisition; Knowledge engineering; Knowledge management; Medical diagnosis; Medical diagnostic imaging; Medical treatment; Rough sets; Inconsistent Medical Data; Knowledge acquisition; Rough;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3497-8

Type

conf

DOI

10.1109/IITA.2008.151

Filename

4739559