An incremental concept formation approach to learn and discover from a clinical database

Author

Soo, Von-Wun ; Wang, Jan-Sing ; Wang, Shih-Pu

Author_Institution

Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2973

Abstract

The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, modified from Lebowitz´s UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class membership and the index-conjunction class membership. The former is a polythetic clustering approach that serves at the early stage of concept formation. The latter that allows only relevant instances to be placed in the same cluster serves as the later stage of concept formation. D-UNIMEM could extract interesting correlation among features from the learned concept hierarchy

Keywords

learning by example; learning systems; medical administrative data processing; pattern classification; relational databases; D-UNIMEM; Lebowitz´s UNIMEM; Percutaneous Transluminal Coronary Angioplasty database; case-based concept formation; clinical database; incremental concept formation; index-conjunction class membership; learned concept hierarchy; polythetic clustering; Angioplasty; Cardiology; Computer science; Data mining; Databases; Diseases; Hospitals; Learning systems; Medical treatment; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374706

Filename

374706