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.

KEYWORDS:

• Alcohol consumption
• Bayesian networks
• physical activity
• screen time

Declaration of interest

The authors declare that they have no competing interest.

Funding

This study was financed partly by a project from the Spanish Ministry of Economy and Competitiveness (TIN2013-42795-P).

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Additional information

Funding

This study was financed partly by a project from the Spanish Ministry of Economy and Competitiveness (TIN2013-42795-P).