Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis

•Supervised generation of Type-2 Fuzzy Classifiers with a new strategy for modeling data uncertainty is proposed.•The methodology combines embedded type-1 membership functions, statistical concepts, and particle swarm optimization.•Classifiers generated with the methodology are compared with Type-2 Fuzzy Classifiers based on symmetric uncertainty.•The performance of the proposed approach is measured with medical diagnosis benchmark data sets with good results.