Extraction of diagnostic knowledge from clinical databases based on rough set theory

Abstract

A rule-induction system, called PRIMEROSE3 (probabilistic rule induction method based on rough sets version 3.0), is introduced. This program first analyzes the statistical characteristics of attribute-value pairs from training samples, then determines what kind of diagnosing model can be applied to the training samples. Then, it extracts not only classification rules for differential diagnosis, but also other medical knowledge needed for other diagnostic procedures in a selected diagnosing model. PRIMEROSE3 is evaluated on three kinds of clinical databases and the induced results are compared with domain knowledge acquired from medical experts, including classification rules. The experimental results show that our proposed method correctly not only selects a diagnosing model, but also extracts domain knowledge.