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Early breast cancer prognosis pbkp_rediction and rule extraction using a new constructive neural network algorithm

Abstract:

Breast cancer relapse pbkp_rediction is an important step in the complex decision-making process of deciding the type of treatment to be applied to patients after surgery. Some non-linear models, like neural networks, have been successfully applied to this task but they suffer from the problem of extracting the underlying rules, and knowing how the methods operate can help to a better understanding of the cancer relapse problem. A recently introduced constructive algorithm (DASG) that creates compact neural network architectures is applied to a dataset of early breast cancer patients with the aim of testing the pbkp_redictive ability of the new method. The DASG method works with Boolean input data and for that reason a transformation procedure was applied to the original data. The degradation in the pbkp_redictive performance due to the transformation of the data is also analyzed using the new method and other standard algorithms. © Springer-Verlag Berlin Heidelberg 2007.