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… CONTINUE READING