- Title
- A global optimisation approach to classification in medical diagnosis and prognosis
- Creator
- Bagirov, Adil; Rubinov, Alex; Yearwood, John; Stranieri, Andrew
- Date
- 2001
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/64607
- Identifier
- vital:3746
- Identifier
-
https://doi.org/10.1109/HICSS.2001.926571
- Abstract
- In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Publisher
- Maui, Hawaii, USA : IEEE
- Relation
- Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Rights
- Copyright IEEE
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0806 Information Systems; Cancer; Convex programming; Medical diagnostic computing; Medical information systems; Visual databases
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