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

fYear

2004

fDate

5-8 Jan. 2004

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;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on

Print_ISBN

0-7695-2056-1

Type

conf

DOI

10.1109/HICSS.2004.1265355

Filename

1265355