The automatic diagnosis system of breast cancer based on the improved Apriori algorithm

Author

Wen-jing Zhang ; Dong-Lai Ma ; Bin Dong

Author_Institution

Coll. of Inf. Sci. & Technol., Agric. Univ. of Hebei, Baoding, China

Volume

1

fYear

2012

fDate

15-17 July 2012

Firstpage

63

Lastpage

66

Abstract

This paper introduces computer-aided diagnosis, association rules and its application in the medical field, and proposes an improved Apriori algorithm combined with the characteristics of breast cancer data. By the mining decision rules, doctors can greatly improve their diagnostic efficiency and accuracy for breast cancer. Using the mining decision rules, we can also establish medical knowledge database, and provide useful data resource for the future medical research.

Keywords

CAD; cancer; data mining; database management systems; medical diagnostic computing; CAD; association rules; automatic diagnosis system; breast cancer data; computer-aided diagnosis; data resource; diagnostic accuracy; diagnostic efficiency; improved Apriori algorithm; medical knowledge database; medical research; mining decision rules; Abstracts; Argon; Breast cancer; Economic indicators; Medical diagnostic imaging; Association Computer-aided diagnosis; Breast cancer;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2012 International Conference on

Conference_Location

Xian

ISSN

2160-133X

Print_ISBN

978-1-4673-1484-8

Type

conf

DOI

10.1109/ICMLC.2012.6358887

Filename

6358887