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Significant cancer prevention factor extraction : an association rule discovery approach
journal contribution
posted on 2017-12-06, 00:00 authored by Jesmin Nahar, Kevin TickleKevin Tickle, A B M Shawkat Ali, YP ChenCancer is increasing the total number of unexpected deaths around the world. Until now, cancer research could not significantly contribute to a proper solution for the cancer patient, and as a result, the high death rate is uncontrolled. The present research aim is to extract the significant prevention factors for particular types of cancer.To find out the prevention factors, we first constructed a prevention factor data set with an extensive literature review on bladder, breast, cervical, lung, prostate and skin cancer. We subsequently employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithms in order to discover most of the significant prevention factors against these specific types of cancer. Experimental results illustrate that Apriori is the most useful association rule-mining algorithm to be used in the discovery of prevention factors.
History
Volume
35Issue
3Start Page
353End Page
367Number of Pages
15ISSN
0148-5598Location
Berlin, HeidelbergPublisher
Springer SciencePublisher DOI
Full Text URL
Language
en-ausPeer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Centre for Intelligent and Networked Systems (CINS); Deakin University; TBA Research Institute;Era Eligible
- Yes