Identifying Risk Groups Associated with Colorectal Cancer

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Identifying Risk Groups Associated with Colorectal Cancer

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by Jie Chen , Hongxing He , Huidong Jin , Damien Mcaullay

BibTeX

@MISC{Chen_identifyingrisk,
    author = {Jie Chen and Hongxing He and Huidong Jin and Damien Mcaullay},
    title = {Identifying Risk Groups Associated with Colorectal Cancer},
    year = {}
}

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Abstract

Abstract. In this paper, we explore data mining techniques for the task of identifying and describing risk groups for colorectal cancer (CRC) from population based administrative health data. Association rule discovery, association classification and scalable clustering analysis are applied to the colorectal cancer patients ’ profiles in contrast to background patients’ profiles. These data mining methods enable us to identify the most common characteristics of the colorectal cancer patients. The knowledge discovered by data mining methods which are quite different from traditional survey approaches. Although it is heuristic, the data mining methods may identify risk groups for further epidemiological study, such as older patients living near health facilities yet seldom utilising those facilities, and with respiratory and circulatory diseases. 1

Keyphrases

colorectal cancer risk group associated data mining method risk group epidemiological study data mining technique association rule discovery common characteristic administrative health data colorectal cancer patient profile traditional survey approach patient profile circulatory disease association classification scalable clustering analysis health facility colorectal cancer patient

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