Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach

Authors:
Jesmin Nahar;Kevin S. Tickle;A. B. Ali;Yi-Ping Phoebe Chen
Affiliations:
School of Computing Sciences, Central Queensland University, Queensland, Australia;School of Computing Sciences, Central Queensland University, Queensland, Australia;School of Computing Sciences, Central Queensland University, Queensland, Australia;Faculty of Science and Technology, Deakin University, Victoria, Australia
Venue:
Journal of Medical Systems
Year:
2011

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Abstract

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.