Exploitation of medical crisp database for fuzzy diagnostic decision support systems

Abstract

There are several types of Diagnostic Decision Support Systems (DDSS) but all move towards a common direction: provide assistance to the doctors/clinicians to make the right diagnosis for a specific patient, minimizing as much as possible the needed time for this. In doing so, some DDSS systems exploit existing crisp medical databases while others take an advantage of human knowledge and experience, by building fuzzy medical databases from scratch. It would be of interest though, as well as time saving, to combine these two together and examine how one can actually use an existing crisp medical dataset to transform it into a new fuzzy medical database with parameters being expressed and defined based on the clinicians' way of thinking. A methodology implementing this task is proposed in this paper accompanied by the results of its application on a real crisp medical database. © 2015 The authors and IOS Press. All rights reserved.