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Significant cancer prevention factor extraction : an association rule discovery approach

journal contribution
posted on 06.12.2017, 00:00 authored by Jesmin Nahar, Kevin TickleKevin Tickle, A B M Shawkat AliA B M Shawkat Ali, YP Chen
Cancer 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

35

Issue

3

Start Page

353

End Page

367

Number of Pages

15

ISSN

0148-5598

Location

Berlin, Heidelberg

Publisher

Springer Science

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Deakin University; TBA Research Institute;

Era Eligible

Yes

Journal

Journal of medical systems.