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

Ali, Mumtaz, Son, Le Hoang, Thanh, Nguyen Dang and Minh, Nguyen Van Minh 2018, A neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures, Applied soft computing journal, vol. 71, pp. 1054-1071, doi: 10.1016/j.asoc.2017.10.012.

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Title A neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures
Author(s) Ali, MumtazORCID iD for Ali, Mumtaz orcid.org/0000-0002-6975-5159
Son, Le Hoang
Thanh, Nguyen Dang
Minh, Nguyen Van Minh
Journal name Applied soft computing journal
Volume number 71
Start page 1054
End page 1071
Total pages 18
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2018-10
ISSN 1568-4946
Summary © 2017 Elsevier B.V. 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.
Language eng
DOI 10.1016/j.asoc.2017.10.012
Indigenous content off
Field of Research 0102 Applied Mathematics
0801 Artificial Intelligence and Image Processing
0806 Information Systems
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2017, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121801

Document type: Journal Article
Collection: Faculty of Science, Engineering and Built Environment
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