Mining Diagnostic Taxonomy for Multi-Stage Medical Diagnosis

Experts' reasoning selects the final diagnosis from many candidates by using hierarchical differential diagnosis. In other words, candidates gives a sophisticated hiearchical taxonomy, usually described as a tree. In this paper, the characteristics of experts' rules are closely examined from the viewpoint of hierarchical decision steps and and a new approach to rule mining with extraction of diagnostic taxonomy from medical datasets is introduced. The key elements of this approach are calculation of the characterization set of each decision attribute (a given\ class) and one of the similarities between characterization sets. From the relations between similarities, tree-based taxonomy is obtained, which includes enough information for hierarchical diagnosis. The proposed method was evaluated on three medical datasets, the experimental results of which show that induced rules correctly represent experts' decision processes.