[1]王潇,郭宗君,季晓云,等.血管性认知障碍发病危险因素预测模型研究[J].青岛大学医学院学报,2017,53(03):253-256.[doi:10.13361/j.qdyxy.201703001]
 WANG Xiao,GUO Zongjun,JI Xiaoyun,et al.A PREDICTIVE MODEL FOR RISK FACTORS FOR VASCULAR COGNITIVE IMPAIRMENT[J].Acta Aacademiae Medicinae Qingdao,2017,53(03):253-256.[doi:10.13361/j.qdyxy.201703001]
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血管性认知障碍发病危险因素预测模型研究()
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《青岛大学医学院学报》[ISSN:1672-4488/CN:37-1356/R]

卷:
第53卷
期数:
2017年03期
页码:
253-256
栏目:
出版日期:
2017-08-07

文章信息/Info

Title:
A PREDICTIVE MODEL FOR RISK FACTORS FOR VASCULAR COGNITIVE IMPAIRMENT
文章编号:
1672-4488(2017)03-0253-04
作者:
王潇郭宗君季晓云王晓林张敏王志宏
青岛大学附属医院老年医学科,山东 青岛 266003
Author(s):
WANG Xiao GUO Zongjun JI Xiaoyun WANG Xiaolin ZHANG Min WANG Zhihong
Department of Geriatrics, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
关键词:
贝叶斯网络认知障碍危险因素预测
Keywords:
Bayesian network cognition disorders risk factors forecasting
分类号:
R741
DOI:
10.13361/j.qdyxy.201703001
文献标志码:
A
摘要:
目的 应用贝叶斯网络分析方法,建立并分析导致血管性认知障碍(VCI)发病的危险因素预测模型。
方法 选取脑血管病病人465例,进行人口学、生活模式和临床疾病因素问卷调查和数据采集,将病人按照10∶1比例随机分为训练集(421例)和测试集(44例),其中训练集病人分为非VCI病人组(225例)和VCI病人组(196例)。训练集数据采用禁忌搜索算法建立贝叶斯网络模型并分析脑血管病病人发生VCI的影响因素,利用测试集数据检验模型预测准确度,同时与传统的Logistic回归模型结果进行比较。
结果 构建的VCI危险因素贝叶斯网络模型预测准确度为67.70%,测试集预测准确度为75.00%。受教育程度、业余爱好、糖尿病与VCI的发生有直接相关关系;饮食、饮茶、饮酒和吸烟等生活行为方式通过影响糖尿病的发生间接影响VCI的发生;体育锻炼等业余爱好不仅直接影响VCI发生,还间接通过影响糖尿病的发生间接影响VCI发生。条件概率表显示,低受教育程度、无业余爱好和糖尿病是脑血管病病人发生VCI的危险因素。Logistic回归分析结果显示,模型预测准确度为66.98%,受教育程度(P=0.005)、饮酒(P=0.001)、体育锻炼(P=0.027)、糖尿病(P=0.012)为发生VCI的独立危险因素。受试者工作特征曲线(ROC曲线)显示,贝叶斯网络模型ROC曲线下面积(AUC)为0.718(95%CI=0.669~0.768);Logistic回归模型的AUC为0.664(95%CI=0.612~0.717)。
结论 应用贝叶斯网络分析方法可以建立VCI危险因素预测模型,其对脑血管病病人VCI发生预测准确度优于Logistic回归模型。
Abstract:
Objective  To establish a predictive model for the risk factors for vascular cognitive impairment (VCI) using Bayesian network.
Methods  A total of 465 patients with cerebrovascular diseases were selected. A questionnaire survey was performed to collect the data on lifestyle and clinical factors, as well as the demographic data. The patients were randomly divided into training set (421 patients) and test set (44 patients) at a ratio of 10∶1, and the patients in the training set were further divided into non-VCI group (225 patients) and VCI group (196 patients). The tabu search algorithm was used to establish a Bayesian network model for the data of the training set, and the influencing factors for the development of VCI in patients with cerebrovascular diseases were analyzed. The data of the test set were used to assess the prediction accuracy of this model, and these results were compared with the results of the conventional logistic regression model.
Results  The Bayesian network model established for the risk factors for VCI had a prediction accuracy of 67.70%, and the prediction accuracy in the test set was 75.00%. Educational level, hobbies, and diabetes were directly associated with the development of VCI. Lifestyle such as eating, drinking tea, drinking, and smoking had indirect influence on the development of VCI via their influence on diabetes. Hobbies including physical exercise not only directly affected the development of VCI, but also had indirect influence via their influence on diabetes. According to the conditional probability tables, low educational level, no hobbies, and diabetes were risk factors for VCI in patients with cerebrovascular diseases. The logistic regression analysis showed that this model had a prediction accuracy of 66.98%, and educational level (P=0.005), drinking (P=0.001), physical exercise (P=0.027), and diabetes (P=0.012) were independent risk factors for VCI. According to the receiver operating characteristic (ROC) curve of the Bayesian network model and the logistic regression model, the Bayesian network model had an area under the ROC curve (AUC) of 0.718 (95% CI=0.669-0.768), while the logistic regression model had an AUC of 0.664 (95% CI=0.612-0.717).
Conclusion  Bayesian network can be used to establish a predictive mo-
del for the risk factors for VCI, and the Bayesian network model is superior to the logistic regression model in the accuracy of predicting VCI in patients with cerebrovascular diseases.
更新日期/Last Update: 2017-08-13