Similarity-Based Pattern Recognition for Disease Symptom Extraction and Characterization.

Neutrosophic Fuzzy Sets (NFS) expand upon classical fuzzy sets in the field of fuzzy set theory by including measures of truth, indeterminacy, and falsity. This paper thoroughly examines the creation and assessment of similarity measures for Single-Valued Neutrosophic Fuzzy Sets (SVNFS). The similarity measure is a crucial metric that quantifies the extent of similarity between two sets. It finds extensive application in various fields such as pattern recognition, medical diagnosis, and decision-making challenges. Nevertheless, the current similarity measures of Neutrosophic Fuzzy Sets(NFS) suffer from limited practicality and interpretation, and do not yield highly reliable outcomes. In order to tackle this issues, We provide a variety of new similarity measures, including the Hausdorff similarity measure, Membership-grade based similarity measure, and Trigonometric Hausdorff similarity measure specifically designed for Neutrosophic Fuzzy Sets(NFS). We conduct a comparison of their performance against existing measures. We verify the efficacy of these approaches by conducting thorough theoretical research and practical trials, showcasing their suitability in pattern recognition. The findings demonstrate substantial enhancements in precision and resilience, offering vital tools for academics and practitioners working with intricate and unpredictable data. The results of our research provide a foundation for future progress in the Neutrosophic Fuzzy Set theory and its practical use in several areas. [ABSTRACT FROM AUTHOR]