Title :
Improving risk grouping rules for prostate cancer patients with optimization
Author :
Churilov, L. ; Bagirov, A.M. ; Schwartz, D. ; Smith, K. ; Dally, M.
Author_Institution :
Sch. of Bus. Syst., Monash Univ., Clayton, Vic., Australia
Abstract :
Data mining techniques provide a popular and powerful toolset to address both clinical and management issues in the area of health care. This paper describes the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. The cluster analysis based model is suggested and an application of non-smooth non-convex optimization techniques to solve this model is discussed. It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.
Keywords :
cancer; data mining; decision making; health care; optimisation; patient treatment; clinical decision making; clinical treatment; cluster analysis; data mining; health care; nonsmooth nonconvex optimization; prostate cancer patient database; risk grouping rules; Breast cancer; Clustering algorithms; Data mining; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Machine learning; Medical services; Neural networks; Prostate cancer;
Conference_Titel :
System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on
Print_ISBN :
0-7695-2056-1
DOI :
10.1109/HICSS.2004.1265355
