Bayesian networks to identify statistical dependencies. A case study of Spanish university students' habits.
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 datase...
| Publicado en: | Informatics for Health & Social Care Vol. 42; no. 2; pp. 166 - 180 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Mar2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121886211&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121886211 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17538157 8P2T jtl: Informatics for Health & Social Care issn: 17538157 maglogo: Y pubinfo: dt: Mar2017 vid: 42 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 121886211 121886211 NLM27245256 121886211 10.1080/17538157.2016.1178117 NLM27245256 121886211 ppf: 166 ppct: 14 formats: tig: atl: Bayesian networks to identify statistical dependencies. A case study of Spanish university students' habits. aug: au: Fuster-Parra, P. Vidal-Conti, J. Borràs, P.A. Palou, P. affil: Department of Mathematics and Computer Science, Universitat Illes Balears, Palma de Mallorca, Baleares, Spain sug: subj: Students Statistics and Numerical Data Colleges and Universities Probability Health Behavior Spain Social Environment Female Body Mass Index Diet Exercise Physical Fitness Young Adult Male Alcohol Drinking Epidemiology Sedentary Behavior Algorithms Adult Human Adult: 19-44 years Female Male ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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