Chen, Jie; He, Hongxing; Jin, Huidong; McAullay, Damien; Williams, Graham; Kelman, Chris
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...[Show more]
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