| Summary: | Extracting usable and useful knowledge from large and complex data sets is a difficult and challenging problem. In this paper, we show how two complementary techniques have been used to tackle this problem in the context of breast cancer. Diagnosis concerns the identification of cancer within a patient; in contrast, prognosis concerns the prediction of the ongoing course of the disease, including issues such as the choice of potential treatments such as chemotherapy or drug therapy, in combination with estimation of chances (or length) of survival. Reliable prognosis depends on many factors, including the identification of the type of this heterogeneous disease. We first use a consensus clustering methodology to identify core, well-characterised sub-groups (or classes) of the disease based on a large database of protein biomarkers from over a thousand patients. We then use fuzzy rule induction and simplification algorithms to generate a simple, comprehensible set of rules for use in future model-based classification. The methods are described and their use is illustrated on real-world data. © ECMS.
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