Methods to determine cut-off between groups 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.
 A: Classification tree(s) and discriminant analysis are two more techniques that might be appropriate.
You can perform discriminant analysis on one independent variable (x). The dependent variable (y) is categorical, in your case it's patient status (diseased or non-diseased).
A: You don't need modelling (logistic regression or mixture models) to establish a cut-off. Just compute a ROC curve of your data and it's going to give you cut-offs. To select only one cut-off specifically, use the methods described by Perkins & Schisterman or simply the Youden Index.



*

*Neil J. Perkins, Enrique F. Schisterman (2006) “The Inconsistency of "Optimal" Cutpoints Obtained using Two Criteria based on the Receiver Operating Characteristic Curve”. American Journal of Epidemiology 163(7), 670–675. DOI: 10.1093/aje/kwj063.

*W. J. Youden (1950) “Index for rating diagnostic tests”. Cancer, 3, 32–35. DOI: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3.

A: I did similar classification problem last fall in the context of churn analysis with our CRM data.  
I used logistic regression, neural networks and decision trees.  
ROC-analysis is one method for evaluation of probabilistic classifications from several models, it is not tied to logistic regression and it uses all cut-offs from range [0,1]. But this was just a start of analysis.  
Next method for evaluation of classifications was setting explicit cost-values for false positives and false negatives. Then I just decided to make a cut-off at point which minimizes total costs.   
Reason why I wanted to have explicit cost values was that our business users could say to me what is relative importance of different false classifications with respect to each other.  
Can your business users say what is relative importance of various cells in the confucion matrix?
