Mining Diagnostic Taxonomy for Multi-Stage Medical Diagnosis
Shusaku Tsumoto · UTAS Research Repository · 2004
Abstract. Experts ’ reasoning selects the final diagnosis from many can-didates by using hierarchical differential diagnosis. In other words, can-didates gives a sophisticated hiearchical taxonomy, usually described as a tree. In this paper, the characteristics of experts ’ rules are closely ex-amined 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 re-sults of which show that induced rules correctly represent experts ’ deci-sion processes.