Bayesian networks to identify statistical dependencies. A case study of Spanish university students' habits. (3rd April 2017)

• Record Type: Journal Article
• Authors: Fuster-Parra, P.; Vidal-Conti, J.; Borràs, P.A.; Palou, P.
• Is Part Of: Informatics for health & social care. Volume 42:Number 2(2017)
• Page Start: 166
• Page End: 179
• Publication Date: 2017-04-03
• Subjects: Alcohol consumption -- Bayesian networks -- physical activity -- screen time; Medicine -- Information services -- Periodicals; Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; 025.0661
• DOI: 10.1080/17538157.2016.1178117
• Languages: English
• ISSNs: 1753-8157

Abstract

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.