Sizing tumors with TNM classifications and rough sets method

Author

Pham, Dzung L.

Author_Institution

Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD, USA

fYear

2004

fDate

24-25 June 2004

Firstpage

221

Lastpage

223

Abstract

In this paper, medical decision support systems for TNM (tumor characteristics, lymph node involvement, and distant metastatic lesions) classification aiming to divide cancer patients to low and high risk patients are presented. In addition, the system also explained the decision in the form of IF-THEN rules and in this manner performed data mining and new knowledge discovery. The case studies show that the system is robust and not dependent on the database size and the noise. The accuracy was almost 80% which is comparable with the accuracy of physicians and much better then obtained with more conventional discriminant analysis (62% and 67%).

Keywords

cancer; data mining; database management systems; decision support systems; logic design; medical diagnostic computing; pattern classification; rough set theory; tumours; IF-THEN rules; TNM classifications; cancer patient; data mining; distant metastatic lesions; knowledge discovery; lymph node involvement; medical decision support systems; rough sets method; tumor characteristics; tumors sizing; Cancer; Data mining; Databases; Decision support systems; Lesions; Lymph nodes; Metastasis; Neoplasms; Noise robustness; Rough sets;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer-Based Medical Systems, 2004. CBMS 2004. Proceedings. 17th IEEE Symposium on

ISSN

1063-7125

Print_ISBN

0-7695-2104-5

Type

conf

DOI

10.1109/CBMS.2004.1311718

Filename

1311718