title: Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.
abstract: An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool.
authors.affiliation: Department of Mathematics and Computer Science, Universitat Illes Balears, Palma de Mallorca, Baleares E-07122, Spain; Research Group on Evidence, Lifestyles & Health, Research Institute on Health Sciences (IUNICS), Universitat Illes Balears, Palma de Mallorca, Baleares E-07122, Spain. Electronic address: pilar.fuster@uib.es.
authors.forename: P
authors.initials: P
authors.lastname: Fuster-Parra
authors.affiliation: Research Group on Evidence, Lifestyles & Health, Research Institute on Health Sciences (IUNICS), Universitat Illes Balears, Palma de Mallorca, Baleares E-07122, Spain.
authors.forename: P
authors.initials: P
authors.lastname: Tauler
authors.affiliation: Research Group on Evidence, Lifestyles & Health, Research Institute on Health Sciences (IUNICS), Universitat Illes Balears, Palma de Mallorca, Baleares E-07122, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Bennasar-Veny
authors.affiliation: Department of Applied Computer Science, AGH University of Science and Technology, Kraków PL-30-059, Poland.
authors.forename: A
authors.initials: A
authors.lastname: Ligęza
authors.affiliation: Prevention of Occupational Risks in Health Services, GESMA, Balearic Islands Health Service, Hospital de Manacor, Manacor, Baleares E-07500, Spain.
authors.forename: A A
authors.initials: AA
authors.lastname: López-González
authors.affiliation: Research Group on Evidence, Lifestyles & Health, Research Institute on Health Sciences (IUNICS), Universitat Illes Balears, Palma de Mallorca, Baleares E-07122, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Aguiló
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015992:Body Mass Index; D001835:Body Weight; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D002319:Cardiovascular System; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D015233:Models, Statistical; D012307:Risk Factors; D012907:Smoking
keywords: Bayesian networks; Cardiovascular lost years; Cardiovascular risk score; Causal dependency discovery; Metabolic syndrome; Model averaging