A neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures

Ali, Mumtaz and Son, Le Hoang and Thanh, Nguyen Dang and Minh, Nguyen Van (2017) A neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures. Applied Soft Computing. ISSN 1568-4946


Medical diagnosis is a procedure for the investigation of a person’s symptoms on the basis of disease. This problem has been investigated and applied to personal healthcare systems in medicine. The relevant methods have limitations regarding neutrosophication, deneutrosophication, similarity measures, correlation coefficients, distance measure, and patients’ history. In this paper, we propose a novel neutrosophic recommender
system for medical diagnosis based on algebraic neutrosophic measures. Specifically, a single-criterion neutrosophic recommender system (SC-NRS) and a multi-criteria neutrosophic recommender system (MC-NRS)
accompanied by algebraic operations such as union, complement and intersection are proposed. Several types
of similarity measures based on the algebraic operations and their theoretic properties are investigated. A prediction formula and a new forecast algorithm using the proposed algebraic similarity measures are designed.
The proposed method is experimentally validated on some benchmark medical datasets against the relevant
ones namely ICSM, DSM, CARE and CFMD. The experiments demonstrate that the proposed method has better Mean Square Error (MSE) than the other algorithms. Besides, there is no large increase in computational time taken by the proposed method and other algorithms. Experiments by various cases of parameters suggest that the MSE values remain almost the same for each dataset when randomly changing the values of parameters in all the medical datasets. Lastly, the strength of all the algorithms is analyzed through ANOVA one-way test and Kruskal-Wallis test. The proposed method has better accuracy than the related algorithms. Experimental results support the advantage and superiority of the proposed method.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online 16 Oct 2017. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 31 Jan 2018 06:34
Last Modified: 02 Feb 2018 01:23
Uncontrolled Keywords: algebraic neutrosophic measures, medical diagnosis, nutrosophic set, neutrosophic recommender system, non-linear forecast model
Fields of Research : 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
01 Mathematical Sciences > 0102 Applied Mathematics > 010202 Biological Mathematics
01 Mathematical Sciences > 0102 Applied Mathematics > 010201 Approximation Theory and Asymptotic Methods
Identification Number or DOI: 10.1016/j.asoc.2017.10.012
URI: http://eprints.usq.edu.au/id/eprint/33644

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