Fig. 2. Classification Algorithm.

The input data for a classification algorithm is a set of labeled feature vectors (i.e. x_i, a feature vector along with its associated class or diagnostic label).The classification algorithm generates a classification rule or decision boundary that best separates the feature vectors that belong to different classes or diagnostic labels. The learned decision rule is then applied to classify new feature vectors from a new participant as belonging to one of the various diagnostic classes.