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Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors
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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830606/ https://www.ncbi.nlm.nih.gov/pubmed/29491353 http://dx.doi.org/10.1038/s41598-018-22167-2 |
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author | Pan, Jinhua Ren, Zeping Li, Wenhan Wei, Zhen Rao, Huaxiang Ren, Hao Zhang, Zhuang Song, Weimei He, Yuling Li, Chenglian Yang, Xiaojuan Chen, LiMin Qiu, Lixia |
author_facet | Pan, Jinhua Ren, Zeping Li, Wenhan Wei, Zhen Rao, Huaxiang Ren, Hao Zhang, Zhuang Song, Weimei He, Yuling Li, Chenglian Yang, Xiaojuan Chen, LiMin Qiu, Lixia |
author_sort | Pan, Jinhua |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5830606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-58306062018-03-05 Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors Pan, Jinhua Ren, Zeping Li, Wenhan Wei, Zhen Rao, Huaxiang Ren, Hao Zhang, Zhuang Song, Weimei He, Yuling Li, Chenglian Yang, Xiaojuan Chen, LiMin Qiu, Lixia Sci Rep Article 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. Nature Publishing Group UK 2018-02-28 /pmc/articles/PMC5830606/ /pubmed/29491353 http://dx.doi.org/10.1038/s41598-018-22167-2 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Pan, Jinhua Ren, Zeping Li, Wenhan Wei, Zhen Rao, Huaxiang Ren, Hao Zhang, Zhuang Song, Weimei He, Yuling Li, Chenglian Yang, Xiaojuan Chen, LiMin Qiu, Lixia Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors |
title | Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors |
title_full | Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors |
title_fullStr | Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors |
title_full_unstemmed | Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors |
title_short | Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors |
title_sort | prevalence of hyperlipidemia in shanxi province, china and application of bayesian networks to analyse its related factors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830606/ https://www.ncbi.nlm.nih.gov/pubmed/29491353 http://dx.doi.org/10.1038/s41598-018-22167-2 |
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