Extracting Diagnostic Knowledge from Hepatitis Dataset by Decision Tree Graph-Based Induction

Abstract

We have proposed a method called Decision Tree Graph-Based Induction (DT-GBI), which constructs a classifier (decision tree) for graph-structured data while simultaneously constructing attributes for classification. Graph-Based Induction (GBI) is utilized in DT-GBI for efficiently extracting typical patterns from graph-structured data by stepwise pair expansion (pairwise chunking). Attributes, i.e., substructures useful for classification task, are constructed by GBI on the fly while constructing a decision tree in DT-GBI. We applied DT-GBI to four classification tasks of hepatitis data using only the time-series data of blood inspection and urinalysis, which was provided by Chiba University Hospital. In the first and second experiments, the stages of fibrosis were used as classes and a decision tree was constructed for discriminating patients with F4 (cirrhosis) from patients with the other stages. In the third experiment, the types of hepatitis (B and C) were used as classes, and in the fourth experiment the effectiveness of interferon therapy was used as class label. The preliminary results of experiments, both constructed decision trees and their predictive accuracies, are reported in this paper. The validity of extracted patterns is now being evaluated by the domain experts (medical doctors). Some of the patterns match experts’ experience and the overall results are encouraging.