Interval Type-2 Fuzzy DSS for Unbiased Medical Diagnosis

Technological innovations coupled with the rapidly expanding amount of medical data digitally collected and stored have made possible to develop advanced Decision Support Systems (DSSs) able to aid physicians in medical diagnosis, by helping them in classifying among different diseases. Building this typology of DSSs preliminarily requires the extraction from huge sets of clinical data of hidden relationships between possible classes and known features. This task is very thorny due to information uncertainties affecting features as well as biases regarding prior probabilities of classes, depending on the environment and conditions of data collection. To address these problems, this paper presents an approach for building an interval type-2 fuzzy DSS able to handle information uncertainties and perform unbiased medical diagnoses, by adapting interval type-2 fuzzy sets to a medical dataset. A proof of concept is given by applying the proposed approach on a benchmark medical dataset. A comparison is performed between i) a type-1 fuzzy system with prior probabilities extracted from the dataset; ii) an interval type-2 fuzzy DSSs modelling non-biased prior probabilities; and iii) the best known classification methods. This comparison is made both when prior probabilities are fixed, i.e. when the system is evaluated by a stratified cross-validation technique, or while no assumption is made about prior probabilities. Results show that the proposed approach reach good performance in most of the cases, thus evidencing the usefulness of extracting an interval type-2 fuzzy system not based on biased prior probabilities, for obtaining more reliable results.