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Prevalence of hyperlipidemia in Shanxi Province China and application of Bayesian networks to analyse its related factors

机译:中国山西省高血脂症患病率及贝叶斯网络分析相关因素的应用

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摘要

This study aimed to obtain the prevalence of hyperlipidemia and its related factors in Shanxi Province, China using multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs). A multi-stage stratified random sampling method was adopted to obtain samples among the general population aged 18 years or above. The prevalence of hyperlipidemia in Shanxi Province was 42.6%. Multivariate logistic regression analysis indicated that gender, age, region, occupation, vegetable intake level, physical activity, body mass index, central obesity, hypertension, and diabetes mellitus are associated with hyperlipidemia. BNs were used to find connections between those related factors and hyperlipidemia, which were established by a complex network structure. The results showed that BNs can not only be used to find out the correlative factors of hyperlipidemia but also to analyse how these factors affect hyperlipidemia and their interrelationships, which is consistent with practical theory, is superior to logistic regression and has better application prospects.
机译:本研究旨在利用多元逻辑回归分析和基于禁忌搜索的贝叶斯网络(BNs)来获得中国山西省的高脂血症患病率及其相关因素。采用多阶段分层随机抽样方法,从18岁以上的普通人群中获取样本。山西省的高脂血症患病率为42.6%。多元逻辑回归分析表明,性别,年龄,地区,职业,蔬菜摄入量,身体活动,体重指数,中枢性肥胖,高血压和糖尿病与高脂血症有关。 BN用于查找那些相关因素与高脂血症之间的联系,这些联系是由复杂的网络结构建立的。结果表明,BNs不仅可以用于发现高脂血症的相关因素,而且可以分析这些因素如何影响高脂血症及其相互关系,这与实际理论相符,优于逻辑回归,具有较好的应用前景。

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