Accuracy and cut-off point selection in three-class classification problems using a generalization of the Youden index.

We study properties of the index J(3), defined as the accuracy, or the maximum correct classification, for a given three-class classification problem. Specifically, using J(3) one can assess the discrimination between the three distributions and obtain an optimal pair of cut-off points c(1)<c(2) in the sense that the sum of the correct classification proportions will be maximized. It also serves as the generalization of the Youden index in three-class problems. Parametric and non-parametric approaches for estimation and testing are considered and methods are applied to data from an MRS study on human immunodeficiency virus (HIV) patients.