title: Bayesian networks to identify statistical dependencies. A case study of Spanish university students' habits.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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 bnlearn package in R language introducing prior knowledge, and the parameters were obtained with Netica software. Three reasoning patterns were considered to make inferences: intercausal, evidential, and causal reasoning.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  Bayesian networks may help to characterize Spanish University students' habits.
authors.affiliation: a Department of Mathematics and Computer Science , Universitat Illes Balears , Palma de Mallorca , Baleares , Spain.
authors.forename: P
authors.initials: P
authors.lastname: Fuster-Parra
authors.affiliation: b Department of Education , Universitat Illes Balears , Palma de Mallorca , Baleares , Spain.
authors.forename: J
authors.initials: J
authors.lastname: Vidal-Conti
authors.affiliation: b Department of Education , Universitat Illes Balears , Palma de Mallorca , Baleares , Spain.
authors.forename: P A
authors.initials: PA
authors.lastname: Borràs
authors.affiliation: b Department of Education , Universitat Illes Balears , Palma de Mallorca , Baleares , Spain.
authors.forename: P
authors.initials: P
authors.lastname: Palou
mesh_terms: D000328:Adult; D000428:Alcohol Drinking / Q000453:epidemiology; D000465:Algorithms; D001499:Bayes Theorem; D015992:Body Mass Index; D004032:Diet; D015444:Exercise; D005260:Female; D015438:Health Behavior; D006801:Humans; D008297:Male; D010809:Physical Fitness; D057185:Sedentary Behavior; D012931:Social Environment; D013030:Spain; D013334:Students / Q000706:statistics & numerical data*; D014495:Universities; D055815:Young Adult
keywords: Alcohol consumption; Bayesian networks; physical activity; screen time