Innovative approaches to multi-attribute decision-making in brain carcinoma diagnosis: a complex q-rung orthopair trapezoidal fuzzy framework and aggregation operator analysis

Abstract

This manuscript tackles brain carcinoma diagnosis through a multi-attribute decision-making lens. Using complex q-rung orthopair trapezoidal fuzzy sets, we develop tailored aggregation operators and explore their significance. We delve into idempotency theory, highlighting instances where monotonicity and boundedness fail. Building on these operators, we propose a novel methodology for fuzzy environment decision-making. Applied to medical diagnosis, we identify the most dangerous brain carcinoma type, showcasing practical utility. Comparative analyses confirm the superiority of our technique, promising advancements in diagnosis methodologies.