Pattern analysis and medical diagnosis based on a novel picture fuzzy similarity measure

While “fuzzy sets”, “intuitionistic fuzzy sets”, “Pythagorean fuzzy sets”, “fermatean fuzzy sets”, and “q-rung orthopair fuzzy sets” often fall short in resolving complex uncertain problems, picture fuzzy sets prove uniquely effective. Such challenges arise in scenarios like employee selection, medical diagnosis, or electoral processes, where outcomes require responses spanning four distinct categories: affirmative, negative, abstention, and rejection. A critical component of picture fuzzy set analysis is robust similarity measures for comparing sets, yet existing methodologies often yield counterintuitive or inconsistent results. To address this gap, we introduce a novel similarity measure for picture fuzzy sets that demonstrates superior efficiency and reliability compared to current approaches. Through rigorous comparative analysis, we validate its enhanced performance and showcase its practical utility across classification tasks, diagnostic systems, and decision-making frameworks.