Prediction of recurrent events in breast cancer using the Naive Bayesian classification

Breast cancer is considered to be the second leading cause of cancer deaths in women today. One of the main problems is to predict recurrent and non-recurrent events, probably more important than the first breast cancer diagnosis. The goal of this paper is to investigate the potential contribution of the Naive Bayesian classification methodology as a reliable support in computer-aided diagnosis of such events, using the well-known Wisconsin Prognostic Breast Cancer dataset. The results showed that the Naive Bayes classifier provides performances equivalent to other machine learning techniques with low computational effort and high speed.