Bayesian Network Analysis

Bayesian networks are probabilistic graphical models representing a network of variables (nodes) and the conditional dependencies between those variables (edges) as a directed acyclic graph. Bayesian networks have been used in various studies, specifically to study cardiometabolic disease and related questions. The structure and parameters of Bayesian networks can be learned from large datasets and incorporate prior knowledge of relationships among variables. Additionally, Bayesian networks can capture non-linear associations between nodes and can also be utilized to conduct queries to estimate the probability of a target node given the values of other nodes

In this study, we construct a Bayesian network model based on 20 years of data from the US National Health and Nutrition Examination Survey (NHANES) to 1) analyze potential pathways that mediate ethnoracial disparities in cardiometabolic health outcomes; 2) understand how the underlying network of variables influencing cardiometabolic health outcomes differs by ethnoracial group; and 3) analyze the differential impact of behavioral factors on cardiometabolic health outcomes by ethnoracial group.