A statistical evaluation of neural computing approaches to predict recurrent events in breast cancer

Breast cancer is considered to be 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 more challenging task than the initial one. In this paper we investigate the potential contribution of intelligent neural networks as a useful tool to support health professionals in diagnosing such events. The neural network algorithms are applied to the breast cancer dataset obtained from Ljubljana Oncology Institute. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perception and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and finally, the classification performances of both algorithms are statistically robust.