Journal article

Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules: Application to prostate cancer patients

L Churilov, A Bagirov, D Schwartz, K Smith, M Dally

JOURNAL OF MANAGEMENT INFORMATION SYSTEMS | ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD | Published : 2005

Abstract

Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and ai..

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