ArticleMedicine2019
Application of a Tabu search-based Bayesian network in identifying factors related to hypertension.
Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 26 citations in OpenAlex.
- Exploring stroke risk factors in different genders using Bayesian networks: a cross-sectional study involving a population of 134,382.BMC public health · 2025Article
- Assessment of Ecological Bridges at Wildlife Crossings in Türkiye: A Case Study of Wild Boar Crossings on the Izmir-Çeşme Motorway.Animals : an open access journal from MDPI · 2023Article
- Using Bayesian networks with Tabu-search algorithm to explore risk factors for hyperhomocysteinemia.Scientific reports · 2023Article
- Exploring factors related to heart attack complicated with hypertension using a Bayesian network model: a study based on the China Health and Retirement Longitudinal Study.Frontiers in public health · 2023Article
- Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.Frontiers in cardiovascular medicine · 2022Article
- Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.BMC public health · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study aimed to study the related factors of hypertension using multivariate logistic regression analysis and tabu search-based Bayesian Networks (BNs). A cluster random sampling method was adopted to obtain samples of the general population aged 15 years or above. Multivariate logistic regression analysis indicated that gender, age, cultural level, body mass index (BMI), central obesity, drinking, diabetes mellitus, Myocardial infarction, Coronary heart disease, Stroke are associated with hypertension. While BNs found connections between those related factors and hypertension were established by complex network structure, age, smoking, occupation, cultural level, BMI, central obesity, drinking, diabetes mellitus, myocardial infarction, coronary heart disease, nephropathy, stroke were direct connection with hypertension, gender was indirectly linked to hypertension through drinking. The results showed that BNs can not only find out the correlative factors of hypertension but also analyze how these factors affect hypertension and their interrelationships, which is consistent with practical theory better than logistic regression and has a better application prospects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.