Application of Graph Theory in the Analysis of Risk Factors for Type 2 Diabetes

Resumo

Type 2 diabetes represents one of the greatest challenges in global public health, due to its complex relationship with metabolic, behavioral, and demographic factors. Traditional statistical methods have shown limitations in analyzing the interdependencies among these risk factors, hindering the identification of relevant patterns for the prevention and management of the disease. This study proposes the application of graph theory as a computational tool to model the connections among clinical variables such as hypertension, obesity, high cholesterol, mental health, and mobility. Using real-world data and correlation analysis techniques, a weighted network of interactions was constructed, allowing a structured and intuitive visualization of these relationships. The results reveal clusters of strongly connected variables that underscore the central role of obesity and hypertension in the type 2 diabetes risk network, as well as relevant associations with psychosocial and functional factors.