- Title
- Improving risk grouping rules for prostate cancer patients with optimization
- Creator
- Churilov, Leonid; Bagirov, Adil; Schwartz, Daniel; Smith, Kate; Dally, Michael
- Date
- 2004
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/45452
- Identifier
- vital:1317
- Identifier
-
https://doi.org/10.1109/HICSS.2004.1265355
- 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.; E1
- Publisher
- Big Island, Hawaii : IEEE
- Relation
- Paper presented at the 37th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii : 5th January, 2004
- Rights
- Copyright IEEE
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Optimization; Prostate cancer
- Full Text
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