I am working with a dataset of diseased vs non-diseased patients. I'm looking for a cut-off value for determining the status of a new patient. This is intended for lab technicians and MDs to allow for quick diagnosis. It's got to be practical, so please spare me from discussing whether it's the right thing to do—I already know it's not a favorable approach.
To be as thorough as possible I want to use multiple methods to determine the cut-off. My hope is that these methods will yield similar values. So far I've used logistic regression (ROC) and mixture modeling. So my question: what other techniques can be used to determine the cut-off?
1/10/2014 EDIT: To be clear - I actually have 3 variables to be classified in the dataset, however only one of them is practically useful. The other two are highly correlated with one another, and not really correlated with the outcome. If I have to use more than one, I will use a ratio, so no need for multivariate classifiers. The goal is to be simple - just one cut-off value.