Learning with Product Units

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Laurens Leerink, C. Giles, Bill Horne, Marwan Jabri


The TNM staging system has been used since the early 1960's to predict breast cancer patient outcome. In an attempt to in(cid:173) crease prognostic accuracy, many putative prognostic factors have been identified. Because the TNM stage model can not accom(cid:173) modate these new factors, the proliferation of factors in breast cancer has lead to clinical confusion. What is required is a new computerized prognostic system that can test putative prognostic factors and integrate the predictive factors with the TNM vari(cid:173) ables in order to increase prognostic accuracy. Using the area un(cid:173) der the curve of the receiver operating characteristic, we compare the accuracy of the following predictive models in terms of five year breast cancer-specific survival: pTNM staging system, princi(cid:173) pal component analysis, classification and regression trees, logistic regression, cascade correlation neural network, conjugate gradient descent neural, probabilistic neural network, and backpropagation neural network. Several statistical models are significantly more ac-