In the field of medicine, several recent studies have shown the value of Artificial Neural Networks, decision trees, logistic regression are playing a major role as the predictor, and classification methods. The research has been expanded to estimate the incidence of breast, lung, liver, ovarian, cervical, bladder and skin cancer. The main aim of this paper is to develop models of logistic regression, Artificial Neural Networks, and Decision trees using the same input and output variables and to compare their success in predicting breast cancer survival in woman. To find the best model for breast cancer survival, the sensitivity and specificity of all these models are measured and evaluated with their respective confidence intervals and the ROC values.
Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees
