A Rough Sets Based Classifier for Primary Biliary Cirrhosis

Author

Revett, Kenneth

Author_Institution

Harrow Sch. of Comput. Sci., Westminster Univ., London

Volume

2

fYear

2005

fDate

21-24 Nov. 2005

Firstpage

1128

Lastpage

1131

Abstract

In this paper, a decision support system is presented based on the machine learning approach of rough sets. The resulting decision support system was able to reduce the dimensionality of the data, produce a highly accurate classifier, and generate a rule based classifier that is readily understood by a domain expert. These preliminary results indicate that the rough sets machine learning approach can be successfully applied to biomedical datasets that contain a variety of attribute types, missing values and multiple decision classes

Keywords

decision support systems; knowledge based systems; learning (artificial intelligence); medical computing; rough set theory; biomedical dataset; dimensionality reduction; medical decision support system; primary biliary cirrhosis; rough set machine learning; rough set-based classifier; Biomedical measurements; Clinical trials; Decision support systems; Ducts; Hospitals; Liver diseases; Machine learning; Medical diagnostic imaging; Rough sets; Testing; cirrhosis diagnosis; dimensionality reduction; medical decision support systems; rough sets;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer as a Tool, 2005. EUROCON 2005.The International Conference on

Conference_Location

Belgrade

Print_ISBN

1-4244-0049-X

Type

conf

DOI

10.1109/EURCON.2005.1630150

Filename

1630150