Nonparametric discriminant analysis applied to medical diagnosis

We present an application of our method for discriminant analysis [3] to the diagnosis of the neurological diseases haemorrhages and infarction due to ischaemia. The method searches for the discriminant directions which maximize the Patrick-Fisher (PF) distance between the projected class-conditional densities. It is a nonparametric method, in the sense that the densities are estimated from the data. Since the PF distance is a highly nonlinear function, we use a recursive optimization procedure for searching the directions corresponding to several large local maxima of the PF distance. The application to the medical dataset indicates the potential of our method for finding a sequence of directions with significant class separation.