A statistical framework for evaluating neural networks to predict recurrent events in breast cancer

Gorunescu, Florin and Gorunescu, Marina and El-Darzi, Elia and Gorunescu, Smaranda (2010) A statistical framework for evaluating neural networks to predict recurrent events in breast cancer. International Journal of General Systems, 39 (5). pp. 471-488. ISSN 0308-1079

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Official URL: http://dx.doi.org/10.1080/03081079.2010.484282

Abstract

Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.

Item Type: Article
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Mr Malcolm Bodger
Date Deposited: 04 Nov 2010 12:38
Last Modified: 08 Jan 2013 16:43
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/8791

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