The New Evidence of Equality of Performance of Classification Tree Method to Discriminant Analysis

For solving the problem of classification/prediction in science, alternative statistical methods have been proposed, such as discriminant analysis, classification trees, neural network, factor analysis, logistic regression, support vector machines and some others tests, all these methods constantly being compared in their performance power. But the results of comparison of abovementioned methods still remain controversial, and hence comparative efficacy of those classifiers has not been established so far. When the authors of this paper explored biogenic amines status in neurologic disorder named dystonia, we also applied some of such statistical methods for data analysis and for elaboration of diagnostic test. The objective of the investigation was to elaborate discrimination of dystonia on the basis of biogenic amines exchange peculiarities. In such situation of creating a new diagnostic tool, the researcher always faces not only the medical problem of identification of some disorder, but also the mathematical problem of comparing efficacy of different methods of calculation, because he usually tries not one, but several statistical approaches, whose accredited comparative power has not been accepted so far. And that is true, of course, for any other science, when elaborating classification/determination tasks. That is why once we addressed ourselves to optimize the diagnosis of some disorder, we unavoidably challenged to explore the mathematical enigma of properties of these alternative methods of calculation, and in our case these alternative methods happened to be the classification tree and the discriminant analysis