Abstract
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.
Highlights
Medical diagnosis is process of investigation of a person’s symptoms on the basis of diseases
It is clearly seen that the Mean Square Error (MSE) of our proposed method is better than ICSM, DSM, CARE and CFMD on the Heart data set while it does not give a reasonable change in MSE with the variants 67, 69, and 71
This paper is dedicated to develop a novel neutrosophic recommender system based on neutrosophic set for medical diagnosis problems which has the ability to predict more accurately during diagnosis process
Summary
Medical diagnosis is process of investigation of a person’s symptoms on the basis of diseases. Starting from the early time of Artificial Intelligence, medical diagnosis has got full attention from both computer science and computer applicable mathematics research society. In this regard, Kononenko [27] in 2001 proposed a process of medical diagnosis which is based on the probability or risk of a person who has a particular state of health in a specific time frame. In 1976, Sanchez [40] applied successfully methods of resolution of the fuzzy relations to the medical diagnosis problems which was further extended by De et al, [12] in 2001 This approach is highly relied on defuzzification method through which the most suitable disease can be determined. We discussed the literature review about medical diagnosis, neutrosophic set; medical diagnosis based on neutrosophic set, recommender system and studied some of their basic properties which will be used in our later pursuit
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