Data Mining with Combined Use of Optimization Techniques and Self-Organizing Maps for Improving Risk Grouping Rules: Application to Prostate Cancer Patients

Authors:
Leonid Churilov;Adyl Bagirov;Daniel Schwartz;Kate Smith;Michael Dally
Affiliations:
School of Business Systems at Monash University, Melbourne;School of Information Technology, and Mathematical Sciences, University of Ballarat, Australia;School of Business Systems, Monash University, Australia;School of Business Systems at Monash University, Australia;Cancer Council of Victoria
Venue:
Journal of Management Information Systems
Year:
2005

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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 aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings.