Building a Bayesian Network to Understand the Interplay of Variables in an Epidemiological Population-Based Study
Paras Multani, Uli Niemann, Mario A. Cypko, Jens‐Peter Kuehn, Henry Voelzke, Steffen Oeltze‐Jafra, Myra Spiliopoulou · 2018
Epidemiological population-based studies collect hundreds of socio-demographic, lifestyle-related, and health related variables for thousands of individuals in order to better characterize health and disease in a defined population. To understand the relations between the variables in the study data, we employ Bayesian Networks, as they not only represent associations between variables but also assign probabilities to these associations. The probabilistic associations allow us to draw inference for unknown events in question, based on the provided evidence. In our work, we induce a Bayesian Network from the data of the population-based epidemiological Study of Health in Pomerania (SHIP), to identify variables related to the outcome "fatty liver". We report on Bayesian Network structure learning, identification of variables associated with the outcome, and the strong associations identified among the variables.