Statistical correlation coefficients for single-valued neutrosophic sets and their applications in medical diagnosis

The concept of single-valued neutrosophic sets (SVNSs) is considered as an attractive tool for dealing with highly ambiguous and uncertain information. The correlation coefficient of SVNSs acts as an important measure in the single-valued neutrosophic set theory and it has been applied in various fields, such as the pattern recognition, medical diagnosis, decision-making and also clustering analysis. To alleviate the weakness of the existing correlation coefficients, a novel statistical correlation coefficient is put forward to measure the degree of correlation between two SVNSs. This statistical correlation coefficient is developed based on the variance and covariance of SVNSs and its value is between −1 and 1. When solving the multicriteria decision making problems, the criteria show different weight values. To consider the weight information of multiple criteria, the weighted statistical correlation coefficient is developed for SVNSs. Afterwards, two numerical examples are given to show the effectiveness of the proposed statistical correlation coefficient in the pattern recognition, which can accurately classify unknown patterns into known patterns. Finally, the feasibility and practicability of the proposed correlation coefficient formula are illustrated by a practical multiple attribute decision making problem of traditional Chinese medicine diagnosis. The comparative results show that the proposed correlation coefficient formula is rational and effective.