Modeling Conditional Relationships in the Management and Monitoring of Type 1 and Type 2 Diabetes through Bayesian Network

Diabetes mellitus is one of the most prevalent chronic diseases, affecting millions of people worldwide. Effective management of diabetes, particularly type 1 (T1DM) and type 2 diabetes (T2DM), requires a deep understanding of the complex interactions between clinical, behavioral, and socio-demographic factors. This study leverages Bayesian networks (BNs) to model these interactions, providing a transparent and interpretable visual framework that reveals how variables such as insulin use, BMI, and socio-economic status influence diabetes outcomes. However, constructing the graphical structure of a BN poses significant challenges due to the intricate and multifaceted relationships involved. To ensure a meaningful comparison between T1DM and T2DM, we utilized a cohort of subjects selected to be as demographically homogeneous as possible. This allowed us to reduce confounding effects and focus on the intrinsic differences between the two conditions. We compared network structures derived from three data samples (50%, 80%, and 100%) to explore how variable relationships evolve as the dataset size increases, ensuring that critical interactions were captured at different levels of data availability. The findings highlight key differences in the management of T1DM and T2DM, particularly with regard to behavioral and socio-economic factors.