Analysis of factors associated with diabetes mellitus in Shanxi Province based on Bayesian network model

WANG Xu-chun; ZHAI Meng-meng; REN Hao; LI Mei-chen; QUAN Di-chen; ZHANG Jie; CHEN Li-min; QIU Li-xia

doi:10.16462/j.cnki.zhjbkz.2021.08.017

Volume 25 Issue 8

Aug. 2021

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2021 > 25(8): 968-974

WANG Xu-chun, ZHAI Meng-meng, REN Hao, LI Mei-chen, QUAN Di-chen, ZHANG Jie, CHEN Li-min, QIU Li-xia. Analysis of factors associated with diabetes mellitus in Shanxi Province based on Bayesian network model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(8): 968-974. doi: 10.16462/j.cnki.zhjbkz.2021.08.017

Citation:

WANG Xu-chun, ZHAI Meng-meng, REN Hao, LI Mei-chen, QUAN Di-chen, ZHANG Jie, CHEN Li-min, QIU Li-xia. Analysis of factors associated with diabetes mellitus in Shanxi Province based on Bayesian network model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(8): 968-974. doi: 10.16462/j.cnki.zhjbkz.2021.08.017

Citation:

WANG Xu-chun, ZHAI Meng-meng, REN Hao, LI Mei-chen, QUAN Di-chen, ZHANG Jie, CHEN Li-min, QIU Li-xia. Analysis of factors associated with diabetes mellitus in Shanxi Province based on Bayesian network model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(8): 968-974. doi: 10.16462/j.cnki.zhjbkz.2021.08.017

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Analysis of factors associated with diabetes mellitus in Shanxi Province based on Bayesian network model

doi: 10.16462/j.cnki.zhjbkz.2021.08.017

1.
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
2.
Central Office, Shanxi Provincial Center for Disease Control and Prevention, Taiyuan 030012, China
3.
Party Committee Office, Shanxi Provincial People's Hospital, Taiyuan 030012, China

Funds:

National Natural Science Foundation of China 81973155

More Information

Corresponding author: QIU Li-xia, E-mail: qlx_1126@163.com
Received Date: 2020-12-18
Rev Recd Date: 2021-03-23

Available Online: 2021-08-24

Publish Date: 2021-08-10

Abstract

Abstract

Objective For the survey data on diabetes in Shanxi Province in 2015, a Bayesian network model of diabetes-related factors was constructed using the max-min hill-climbing (MMHC) algorithm to explore the network relationships between diabetes and its related factors, and the strength of each influencing factor on diabetes was reflected through network model inference. Methods Single-factor analysis and multi-factor logistic regressions were used to initially screen the variables for survey data on diabetes mellitus among residents aged 18 years and above in Shanxi Province. Afterwards, a Bayesian network was constructed with the MMHC algorithm, and the parameters were estimated by great likelihood estimation. Results The detection rate of diabetes mellitus in Shanxi Province in 2015 stood at 9.5%. After logistic regression feature screening, eight variables, namely age, occupation, average daily oil intake, hypertension, hyperlipidaemia, BMI and heart rate, were finally entered into the model. The Bayesian network model demonstrated that age, hyperlipidaemia and hypertension were directly related to diabetes; BMI was indirectly related to diabetes by hyperlipidaemia, and the average daily oil intake indirectly affected diabetes by BMI and hyperlipidaemia. Conclusion Bayesian network models can well reveal the complex network relationships between diabetes and its associated factors and have a good applicability and prospects in the analysis of disease-related factors.
- Diabetes mellitus,
- Relevant factors,
- Max-min hill-climbing algorithm,
- Bayesian network model

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References(26)

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