Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support

Abstract

One of the two goals of this paper is to briefly present two different methodologies that can be used to the design of intelligent decision support systems, in particular, from the field of medicine. The first approach, combining artificial neural networks and fuzzy sets, yields a neuro-fuzzy classifier that can be trained with both purely numerical data as well as qualitative, linguistic, fuzzy data that describe the decision-making process. The second approach – resulting in a rough classifier – combines all positive aspects of rule induction systems with the flexibility of statistical techniques for classification. The second goal of this paper is to perform a broad comparative analysis of both proposed methodologies (and two others) applied to: (a) the problem of selecting surgical and non-surgical cases in the veterinary domain of equine colic, (b) the problem of diagnosing benign and malign types of breast cancer, and (c) the problem of corporate bankruptcy prediction (corporate `financial health'). Several aspects of comparison have been considered including the accuracy of the systems, diversity of the data processed, transparency and the form of decisions made.