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Brain cancer diagnosis-association rule based computational intelligence approach

conference contribution
posted on 27.10.2020, 00:00 by J Nahar, ABMS Shawkat Ali, Tasadduq ImamTasadduq Imam, Kevin TickleKevin Tickle, P Chen
This research employs a computational intelligence based approach to identify the risk factors of brain cancer. More specifically this research utilizes association rule mining techniques to determine the risk factors derived from the brain cancer literature. The research also develops a novel database extracting data from existing literature. Arguably, the outcomes may aid designing brain cancer diagnostic systems and contribute to early diagnosis of the mortal disease. Additionally, it demonstrates the effectiveness of computational intelligence techniques in cancer disease diagnosis. The benefits of this research could apply to different stakeholders such as the CAD (Computer aided diagnosis) designers and the data mining community, the medical field and in particular general practitioners as well as the general public. © 2016 IEEE.

History

Start Page

89

End Page

95

Number of Pages

7

Start Date

08/12/2016

Finish Date

10/12/2016

ISBN-13

9781509043149

Location

Nadi, Fiji

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

The University of Fiji; La Trobe University

Era Eligible

Yes

Name of Conference

2016 IEEE International Conference on Computer and Information Technology (CIT 2016)