Rough Set Based Classification rules generation for SARS Patients

Author

Honghai, Feng ; Guoshun, Chen ; Yufeng, WANG ; Bingru, Yang ; Yumei, Chen

Author_Institution

Hebai Agric. Univ., Beijing

fYear

2005

fDate

6/27/1905 12:00:00 AM

Firstpage

6977

Lastpage

6980

Abstract

SARS is an acute infectious disease and can cause a large amount of death. Up until now we have not known it well. With the experimental results of micronutrients of 30 SARS patients and 30 non-SARS patients, using rough set theory we induce some classification rules. Attribute reduction results show that micronutrients Fe, Ca, K and Na are necessary and sufficient for classification, whereas micronutrients Zn, Cu and Mg are not necessary or are redundant. Additionally, we find that micronutrient Ca has a strong correlation to SARS. The classification results of 30 other examples show that the rough set classification method is available

Keywords

calcium; diseases; iron; medical diagnostic computing; patient diagnosis; potassium; rough set theory; sodium; Ca; Cu; Fe; K; Mg; Na; SARS patients; Zn; acute infectious disease; attribute reduction; rough set based classification rules generation; rough set theory; Artificial neural networks; Chemical technology; Data mining; Delta modulation; Diseases; Knowledge acquisition; Set theory; Support vector machines; Unmanned aerial vehicles; Zinc;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1616111

Filename

1616111