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Brain cancer diagnosis-association rule based computational intelligence approach
conference contribution
posted on 2020-10-27, 00:00 authored by J Nahar, ABMS Shawkat Ali, Tasadduq ImamTasadduq Imam, Kevin Tickle, P ChenThis 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
89End Page
95Number of Pages
7Start Date
2016-12-08Finish Date
2016-12-10ISBN-13
9781509043149Location
Nadi, FijiPublisher
IEEEPlace of Publication
Piscataway, NJPublisher DOI
Full Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
The University of Fiji; La Trobe UniversityEra Eligible
- Yes
Name of Conference
2016 IEEE International Conference on Computer and Information Technology (CIT 2016)Usage metrics
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