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

^1. Based on ‘A statistical evaluation of neural computing approaches to predict recurrent events in breast cancer’, by F. Gorunescu, M. Gorunescu, E. El-Darzi and S. Gorunescu, which appeared in the Proceedings of the 4th International IEEE Conference on Intelligent Systems IS’08, pp. 11-38–11-43.

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.

Keywords:

• breast cancer
• recurrent events
• neural network models
• statistical analysis

Acknowledgements

The first breast cancer database was obtained from the Institute of Oncology, University Medical Centre, Ljubljana, Slovenia, and we thank M. Zwitter and M. Soklic for providing the data. The second breast cancer database was obtained from the University of Wisconsin Hospitals, Madison, WI, USA and the authors are grateful to Dr William H. Wolberg, W. Nick Street and Olvi L. Mangasarian for providing those data.

Notes

^1. Based on ‘A statistical evaluation of neural computing approaches to predict recurrent events in breast cancer’, by F. Gorunescu, M. Gorunescu, E. El-Darzi and S. Gorunescu, which appeared in the Proceedings of the 4th International IEEE Conference on Intelligent Systems IS’08, pp. 11-38–11-43.