Purging false negatives in cancer diagnosis using incremental active learning
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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tasis) is a widely used staging system for predicting the outcome of cancer patients. However, the TNM is not accurate in prediction, partially due to the fact of deficient staging within and between stages. Based on the availability of large cancer patient datasets, there is a need to expand the TNM. In this paper, we present a general clustering-based approach to accomplish this task of expansion. This approach admits multiple factors. One major advantage of the approach is that patients within each generated group are homogeneous in terms of survival, so that a more accurate prediction of outcome of patients can be made. A demonstration of use of the proposed method is given for breast cancer patients. Key words: TNM, clustering, breast cancer, survival curve