Bayesian networks to identify statistical dependencies. A case study of Spanish university students’ habits

<i>Objective</i>: The present study sought to discover the relationships among different features characterizing Spanish university students’ habits through a Bayesian network (BN). The set of features with the strongest influence in specific features can be determined. <i>Methods</i>: A BN was built from a dataset composed of 13 relevant features, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure was learned with the <i>bnlearn</i> package in R language introducing prior knowledge, and the parameters were obtained with Netica software. Three reasoning patterns were considered to make inferences: <i>intercausal, evidential</i>, and <i>causal</i> reasoning. <i>Results</i>: BN determined the different relationships. Through inference several conclusions were achieved, for instance a high probability value of physical activity in low state was obtained when active peers were instantiated to none state, self-rated fitness to fair state, bmi to normal weight, sitting time to moderate, age to 22–25, and gender to woman state. <i>Conclusions</i>: Bayesian networks may help to characterize Spanish University students’ habits.