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.
Highlights
Cardiovascular and cerebrovascular disease (CVD) is the leading disease that threatens human health worldwide and has become one of the leading causes of death [1, 2]
The 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
We aim to explore a novel hybrid algorithm of BN to portray the inherent relationships between hyperlipidemia and its associated factors, to predict the risk of hyperlipidemia in BNs model, to compare the effects of Logistic regression model and BN model on results interpretation and risk reasoning, and to provide a new model construction method for the study of factors affecting hyperlipidemia
Summary
Cardiovascular and cerebrovascular disease (CVD) is the leading disease that threatens human health worldwide and has become one of the leading causes of death [1, 2]. Atherosclerosis is the main cause of CVD. Hyperlipidemia, as the most important risk factor for atherosclerosis, plays an important role in the occurrence and development of CVD [3, 4]. Hyperlipidemia has become an important public health problem, and according to existing studies, prevention and control of hyperlipidemia can play a significant role in the first- and seconddegree prevention of cardiovascular disease [7, 8]. It is important to comprehensively analyze the related factors of hyperlipidemia and the complex relationship between these factors to prevent the occurrence of hyperlipidemia. 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)
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