On the performance evaluation of intuitionistic vector similarity measures for medical diagnosis 1

Abstract

Intuitionistic fuzzy recommender system (IFRS), which has been recently presented based on the theories of intuitionistic fuzzy sets and recommender systems, is an efficient tool for medical diagnosis. IFRS used the intuitionistic fuzzy similarity degree (IFSD) regarded as the generalization of the hard user-based, item-based and the rating-based similarity degrees in recommender systems to calculate the analogousness between patients in the system. In this paper, we firstly extend IFRS by using a new term - the intuitionistic fuzzy vector (IFV) instead of the existing intuitionistic fuzzy matrix (IFM) in IFRS. Then, the intuitionistic value similarity measure (IvSM) and the intuitionistic vector similarity measure (IVSM) are defined on the basis of the intuitionistic fuzzy vector. Some mathematical properties of these new terms are examined, and several IVSM functions are proposed. The performances of these IVSM functions for medical diagnosis are experimentally validated and compared with the existing similarity degrees of IFRS. The suggestion and recommendation of this paper involve the most efficient IVSM function(s) that should be used for medical diagnosis.