Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Abstract

An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.

Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes

“…A novel fuzzy classification method related to a Mamdani-type fuzzy inference focuses on the induction of fuzzy rules from interval type-2 FIS (IT2FIS) [4]. The authors [13] proposed the design of interval type-3 FIS (IT3FIS) using a GA to find the main FIS parameters for medical classification. The results were compared with type-1 FISs, IT2FISs, and general type-2 FISs (GT2FISs).…”