Improving Healthcare Strategies with t-Fuzzy Graphs: A Decision Support Modelper

Abstract

Health management systems require intelligent decision-making to effectively analyze complex interactions between various medical, financial, and operational factors. This study explores the application of t-Fuzzy Graphs (t-FG) in modeling and managing intricate relationships within healthcare environments. By leveraging t-FG, this research highlights how these graphs can express uncertainty, capture multi-dimensional dependencies, and provide a structured representation of diverse health management variables. Fundamental t-FG operations, including homomorphism and isomorphism, are introduced to demonstrate their role in optimizing decision-making processes. Furthermore, the study discusses real-world applications of t-FG in healthcare, showcasing their potential in handling circular dimensions such as resource allocation, patient care strategies, and financial planning. The adaptability and efficiency of t-FG make them a valuable tool for policy development and strategic decision-making in health management, particularly in addressing complex social and numerical challenges within the healthcare sector.