基于贝叶斯网络模型的山西省糖尿病相关因素

王旭春; 翟梦梦; 任浩; 李美晨; 全帝臣; 张洁; 陈利民; 仇丽霞

doi:10.16462/j.cnki.zhjbkz.2021.08.017

基于贝叶斯网络模型的山西省糖尿病相关因素

doi: 10.16462/j.cnki.zhjbkz.2021.08.017

1.
030001 太原，山西医科大学公共卫生学院卫生统计学教研室
2.
030012 太原，山西省疾病预防控制中心中心办公室
3.
030012 太原，山西省人民医院党委办公室

基金项目:

国家自然科学基金 81973155

详细信息

通讯作者:
仇丽霞，E-mail：qlx_1126@163.com

中图分类号: R587.1
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- 被引次数: 0
出版历程
- 收稿日期: 2020-12-18
- 修回日期: 2021-03-23
- 网络出版日期: 2021-08-24
- 刊出日期: 2021-08-10

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

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

摘要

摘要: 目的针对2015年山西省糖尿病调查数据，利用最大最小爬山(max-min hill-climbing, MMHC)算法构建糖尿病相关因素的贝叶斯网络模型，探索糖尿病及其相关因素间的网络关系，通过网络模型推理反映各影响因素对糖尿病的影响程度。方法采用单因素及多因素Logistic回归分析模型对2015年山西省≥18岁居民的糖尿病调查数据进行变量初筛，再以MMHC算法构建贝叶斯网络模型，参数估计采用极大似然估计法。结果 2015年山西省糖尿病的检出率是9.5%。经Logistic回归分析模型对变量进行筛选后，年龄、职业、日均摄油量、高血压、高脂血症、BMI和心率被纳入贝叶斯网络模型；贝叶斯网络模型结果显示：年龄、高脂血症、高血压与糖尿病直接相关，BMI通过影响高脂血症与糖尿病间接相关，日均摄油量通过影响BMI和高脂血症与糖尿病间接相关。结论贝叶斯网络模型能很好地揭示糖尿病及其相关因素间复杂的网络关系，在分析疾病相关因素上具有较好的适用性和应用前景。
- 糖尿病 /
- 相关因素 /
- 最大最小爬山算法 /
- 贝叶斯网络模型
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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图 1 2015年山西省DM调查点分布图

Figure 1. Distribution of survey points of diabetes mellitus in Shanxi Province in 2015

下载: 全尺寸图片幻灯片

图 2 MMHC算法构建DM贝叶斯网络模型及先验概率

Figure 2. Diabetes mellitus Bayesian network constructed by MMHC algorithm and its prior probability

下载: 全尺寸图片幻灯片

表 1 2015年山西省DM患病率的单因素分析[n (%)]

Table 1. Univariate analysis of the prevalence of diabetes mellitns in Shanxi Province in 2015 [n (%)]

变量	N(%)	糖尿病		χ²值	P值	变量	N(%)	糖尿病		χ²值	P值
变量	N(%)	是	否	χ²值	P值	变量	N(%)	是	否	χ²值	P值
民族				1.998	0.157	日均摄油量(g)				5.941	0.015
汉族	4 548(99.6)	433(9.5)	4 115(90.5)			≤25	238(5.2)	50(7.0)	662(93.0)
少数民族	19(0.4)	0(0.0)	19(100.0)			>25	4 329(94.8)	383(9.9)	3 472(90.1)
性别				2.296	0.130	日均摄盐量(g)				0.098	0.754
男	2 236(49.0)	197(8.8)	2 039(91.2)			≤6	236(5.2)	21(8.9)	215(91.1)
女	2 331(51.0)	236(10.1)	2 095(89.9)			>6	4 331(94.8)	412(9.5)	3 919(90.5)
年龄(岁)				86.867	< 0.001	高血压				40.103	< 0.001
18~ < 40	549(12.0)	24(4.4)	525(95.6)			否	2 500(54.7)	158(6.3)	2 342(93.7)
40~ < 60	2 202(48.2)	148(6.7)	2 054(93.3)			是	2 067(45.3)	275(13.3)	1 792(86.7)
≥60	1 816(39.8)	261(14.4)	1 555(85.6)			高脂血症				34.012	< 0.001
婚姻状况				9.329	0.009	否	4 134(90.5)	175(7.1)	2 278(92.9)
未婚	160(3.5)	9(5.6)	151(94.4)			是	433(9.5)	258(12.2)	1 856(87.8)
已婚	4 178(91.5)	391(9.4)	3 787(90.6)			心率				12.872	0.002
离异/丧偶	229(5.0)	33(14.4)	196(85.6)			过缓	156(3.5)	14(9.0)	142(91.0)
职业				64.142	< 0.001	正常	4 156(92.2)	381(9.2)	3 775(90.8)
农牧渔业	2 324(50.9)	175(7.5)	2 149(92.5)			过速	196(4.3)	33(16.8)	163(83.2)
其他劳动	1 025(22.4)	89(8.7)	936(91.3)			慢性消化系统疾病				0.135	0.713
未就业	127(2.8)	14(11.0)	113(89.0)			无	752(16.5)	74(9.8)	678(90.2)
自由职业	766(16.8)	87(11.4)	679(88.6)			有	3 815(83.5)	359(9.4)	3 456(90.6)
离退休	325(7.1)	68(20.9)	257(79.1)			慢性泌尿系统疾病				3.258	0.071
文化水平				0.419	0.518	无	325(7.1)	40(12.3)	285(87.7)
高中以下	3 692(80.8)	345(9.3)	3 347(90.7)			有	4 242(92.9)	393(9.3)	3 849(90.7)
高中及以上	875(19.2)	88(10.1)	787(89.9)			中心性肥胖				35.490	< 0.001
吸烟				8.890	0.003	否	1 966(43.0)	128(6.5)	1 838(93.5)
否	3 184(69.7)	329(10.3)	2 855(89.7)			是	2 601(57.0)	305(11.7)	2 296(88.3)
是	1 383(30.3)	104(7.5)	1 279(92.5)			BMI (kg/m²)				46.716	< 0.001
饮酒				12.428	0.006	< 18.5	127(2.8)	6(4.7)	121(95.3)
从不	4 247(93.0)	420(9.9)	3 827(90.1)			18.5~ < 24.0	1 926(42.2)	136(7.1)	1 790(92.9)
偶尔	40(0.9)	3(7.5)	37(92.5)			24.0~ < 28.0	1 715(37.6)	170(9.9)	1 545(90.1)
经常	151(3.3)	5(3.3)	146(96.7)			≥28.0	799(17.5)	121(15.1)	678(84.9)
每天	129(2.8)	5(3.9)	124(96.1)
身体活动度				10.394	0.004
不足	1 032(22.6)	110(10.7)	922(89.3)
达标	2 381(52.1)	241(10.1)	2 140(89.9)
充足	1 154(25.3)	82(7.1)	1 072(92.9)

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表 2 2015年山西省DM患病的多因素Logistic回归分析

Table 2. Multi-factor Logistic regression analysis of diabetes mellitns prevalence in Shanxi Province in 2015

变量	β值	s_x值	Wald χ²值	OR(95% CI)值	P值	变量	β值	s_x值	Wald χ²值	OR(95% CI)值	P值
年龄(岁)						高脂血症
18~ < 40				1.000		否				1.000
40~ < 60	0.426	0.237	3.232	1.532(0.962~2.438)	0.072	是	0.487	0.109	19.949	1.627(1.314~2.015)	< 0.001
≥60	1.200	0.241	24.866	3.321(2.072~5.324)	< 0.001	心率
职业						过缓				1.000
农牧渔业				1.000		正常	0.109	0.294	0.138	1.115(0.627~1.983)	0.710
其他劳动	0.361	0.143	6.417	1.435(1.085~1.898)	0.011	过速	0.790	0.353	5.013	2.204(1.104~4.403)	0.025
未就业	0.645	0.315	4.193	1.907(1.028~3.537)	0.041	BMI (kg/m²)
自由职业	0.405	0.144	7.882	1.499(1.130~1.988)	0.005	< 18.5				1.000
离退休	0.934	0.166	31.680	2.545(1.838~3.523)	< 0.001	18.5~ < 24.0	0.464	0.434	1.143	1.590(0.679~3.722)	0.285
日均摄油量(g)						24.0~ < 28.0	0.710	0.435	2.669	2.035(0.868~4.772)	0.102
≤25				1.000		≥28.0	1.085	0.442	6.043	2.961(1.246~7.035)	0.014
>25	0.370	0.164	5.116	1.448(1.051~1.996)	0.023	常数	-5.279	0.633	69.566	0.005	< 0.001
高血压
否				1.000
是	0.402	0.115	12.207	1.494(1.193~1.872)	< 0.001

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表 3 2015年山西省DM节点的条件概率表

Table 3. Conditional probability table of diabetes mellitus nodes in Shanxi Province in 2015

高脂血症	年龄(岁)	DM患病风险
否	18~ < 40	0.018
否	40~ < 60	0.051
否	≥60	0.112
是	18~ < 40	0.072
是	40~ < 60	0.086
是	≥60	0.182

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