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.
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