Are neural networks best used to help logistic regression? An example from breast cancer survival analysis

Artificial neural networks are popularly used as universal nonlinear inference models. However, they suffer from two major drawbacks. Their operation is opaque because of the distributed nature of the representations they form, and this makes it different to interpret what they do. Worse still, there are no clearly accepted models of generality which makes it difficult to demonstrate reliability when applied to future data. In this paper neural networks generate hypotheses concerning interaction terms which are integrated into standard statistical models that are linear in the parameters, where the significance of the nonlinear terms and the generality of the model, can be assured using well established statistical tests