Digital Library of the European Council for Modelling and Simulation

Title:	Consensus Clustering And Fuzzy Classification For Breast Cancer Prognosis
Authors:	Jonathan M. Garibaldi, Daniele Soria, Khairul A. Rasmani
Published in:	(2010).ECMS 2010 Proceedings edited by A Bargiela S A Ali D Crowley E J H Kerckhoffs. European Council for Modeling and Simulation. doi:10.7148/2010 ISBN: 978-0-9564944-1-2 Doi: 10.7148/2010 24^th European Conference on Modelling and Simulation, Simulation Meets Global Challenges Kuala Lumpur, June 1-4 2010
Citation format:	Garibaldi, J. M., Soria, D., & Rasman, K. A. (2010). Consensus Clustering And Fuzzy Classification For Breast Cancer Prognosis. ECMS 2010 Proceedings edited by A Bargiela S A Ali D Crowley E J H Kerckhoffs (pp. 15-22). European Council for Modeling and Simulation. doi:10.7148/2010-0015-0022
DOI:	http://dx.doi.org/10.7148/2010-0015-0022
Abstract:	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 tech- niques have been used to tackle this problem in the con- text of breast cancer. Diagnosis concerns the identifica- tion of cancer within a patient; in contrast, prognosis con- cerns the prediction of the ongoing course of the disease, including issues such as the choice of potential treat- ments such as chemotherapy or drug therapy, in combi- nation with estimation of chances (or length) of survival. Reliable prognosis depends on many factors, including the identification of the type of this heterogeneous dis- ease. We first use a consensus clustering methodol- ogy to identify core, well-characterised sub-groups (or classes) of the disease based on a large database of pro- tein 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 de- scribed and their use is illustrated on real-world data.
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