ArticleBMC public health2021
Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.
Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 26 citations in OpenAlex.
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- Associations Between Personality Traits, Self-Efficacy and Complementary Feeding Behavior Among Infant Caregivers in Western Rural China.Maternal & child nutrition · 2025Article
- Exploring the causal effects of physical activity, diet, and nutrition on hypertension and hyperlipidemia: a multivariable Mendelian randomization analysis.BMC cardiovascular disorders · 2025Article
- Assessing Credibility in Bayesian Networks Structure Learning.Entropy (Basel, Switzerland) · 2024Article
- Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis: a cross-sectional and longitudinal study.BMC public health · 2024Article
- Do the New Rural Pension Scheme promote the health status of chronic patients in old age? -Evidence from CHARLS 2018 in China.BMC public health · 2023Article
- Construction of predictive model for osteoporosis related factors among postmenopausal women on the basis of logistic regression and Bayesian network.Preventive medicine reports · 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
- The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population.BMC geriatrics · 2022Article
- Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThis article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs).
methodsLogistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model.
resultsThe BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia.
conclusionsThe BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.
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