A tree-based decision rule for identifying profile groups of cases without predefined classes: application in diffuse large B-cell lymphomas.

Abstract

In this paper, we examined the utility of a forward growing classification tree as a supplement to cluster analysis for deriving a decision rule for the identification of profile groups when the cases do not belong to predefined classes. The technique was applied for the identification of low and high proliferation profile groups of diffuse large B-cell lymphomas according to the immunohistochemical expression levels of proliferation proteins. In a forward growing classification tree method, the size of the tree is controlled by the improvement (threshold value) in the apparent misclassification rate after each split. The classes used in the tree were defined using k-means clustering. The decision rule consisted of the splitting points of the split variables used. The methodology was applied to the histology data from 79 cases of diffuse large B-cell lymphomas. Ten classes of individual cases were derived from k-means clustering. Then, a classification tree with a threshold of 2% was used to derive the decision rule. Branches at the left side of the tree consisted of individuals with a low proliferation profile and branches at the right side of the tree consisted of cases with a high proliferation profile. The classification tree, as a supplement method, not only identified but also provided decision rules for identifying profile groups. Finally, it also allowed for exploration of the data structure.