Diagnosing breast cancer from FNAs: variable relevance in neural network and logistic regression models.
We compared the selection of variables for building a classification model for the diagnosis of breast cancer using neural networks and logistic regression. A set of 460 cases was used to build neural network and logistic regression models that classify cell samples obtained by fine-needle aspiration (FNA) as malignant or benign, depending on nine pathology features. Variables selected by a step down logistic regression model were compared to those selected by a measure of relevance derived from neural network weights. Since both types of models resulted in similar predictive accuracy, we expected approximately the same variables to be selected. The variables with the highest relevance values for the neural network models corresponded to those of high significance in univariate logistic regression models, but were not the ones selected in the step down procedure of multivariate models. Variable relevance based on weights for neural network models does not seem to be a consistent index of the importance of that variable for multivariate models such as logistic regression.
Ohno-Machado
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Decision Systems Group, Brigham and Women's Hospital, Boston, USA. machado@dsg.harvard.edu
Bialek
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D000704:Analysis of Variance; D001707:Biopsy, Needle; D001943:Breast Neoplasms / Q000473:pathology*; D005260:Female; D006801:Humans; D016015:Logistic Models; D015999:Multivariate Analysis; D016571:Neural Networks, Computer