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Breast Cancer Prognosis via Gaussian Mixture Regression

This paper compares the performance of classification and regression trees (CART), multivariate adaptive regression splines (MARS), and a Gaussian mixture regressor (GMR) method in predicting breast cancer recurrence time in patients that have undergone cancer excision. It is shown that the GMR-based algorithm demonstrates an improved performance compared to CART and MARS. Moreover, GMR performance is comparable to that of a baseline predictor with the advantage of performing automatic feature selection and model optimization