Bayesian Networks, Rule Induction and Logistic Regression in the Prediction of Women Survival Suuering from Breast Cancer

In this paper we present an empirical comparison between several paradigms coming from Statistics and Artiicial Intelligence for solving a supervised classiication problem. The empirically compared paradigms are Bayesian Networks, Rule Induction and Logistic Regression. The problem to tackle is the prediction of women survival diagnosed with breast cancer taking into account four predictor variables gathered at the moment of the diagnosis. The data le includes 1000 diagnosed cases at the Oncological Institute of Gipuzkoa (Basque Country). The validation of the paradigms was carried out using the 10-fold cross-validation method.