Intuitionistic fuzzy recommender systems: An effective tool for medical diagnosis
Medical
diagnosis has been being considered as one of the important processes
in clinical medicine that determines acquired diseases from some given
symptoms. Enhancing the accuracy of diagnosis is the centralized focuses
of researchers involving the uses of computerized techniques such as
intuitionistic fuzzy sets (IFS) and recommender systems (RS). Based upon
the observation that medical data are often imprecise, incomplete and
vague so that using the standalone IFS and RS methods may not improve
the accuracy of diagnosis, in this paper we consider the integration of
IFS and RS into the proposed methodology and present a novel
intuitionistic fuzzy recommender systems (IFRS) including: (i) new
definitions of single-criterion and multi-criteria IFRS; (ii) new
definitions of intuitionistic fuzzy matrix (IFM) and intuitionistic
fuzzy composition matrix (IFCM); (iii) proposing intuitionistic fuzzy
similarity matrix (IFSM), intuitionistic fuzzy similarity degree (IFSD)
and the formulas to predict values on the basis of IFSD; (iv) a novel
intuitionistic fuzzy collaborative filtering method so-called IFCF to
predict the possible diseases. Experimental results reveal that IFCF
obtains better accuracy than the standalone methods of IFS such as De et
al., Szmidt and Kacprzyk, Samuel and Balamurugan and RS, e.g. Davis et
al. and Hassan and Syed. (C) 2014 Elsevier B.V. All rights reserved.
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