A Dynamic Bayesian Network model for simulating the progression to diabetes onset in the ageing population

This work presents a tool based on a Dynamic Bayesian Network (DBN) model to simulate the progression to type 2 diabetes (T2D) onset in the ageing population. Including longitudinally collected features characterizing different aspects of the ageing process, we dynamically model the relationships among the variables and the outcome over time, obtaining a network that shows a direct joined effect of glycated hemoglobin and body mass index (BMI) on the T2D onset. Remarkably, DBNs present a broad interpretability regardless of their complexity. We also employ the model to assess the impact of modifiable risk factors on developing the disease, showing how an increased BMI leads to an augmented T2D risk.