决策树分析在急性心肌梗死事件预测中的应用

doi:10.3785/j.issn.1008-9292.2019.12.02

浙江大学学报（医学版）

2019, Vol. 48

Issue (6): 594-602 DOI: 10.3785/j.issn.1008-9292.2019.12.02

原著

决策树分析在急性心肌梗死事件预测中的应用

张圣¹(

),胡振杰²,叶璐³,郑亚如^4,*(

)

1. 浙江省人民医院杭州医学院附属人民医院神经内科, 浙江杭州 310014
2. 中国人民解放军联勤保障部队第九○六医院呼吸与重症医学科, 浙江宁波 315040
3. 浙江大学医学院精神卫生中心暨杭州市第七人民医院检验科, 浙江杭州 310013
4. 浙江省人民医院杭州医学院附属人民医院心血管内科, 浙江杭州 310014

Application of Logistic regression and decision tree analysis in prediction of acute myocardial infarction events

ZHANG Sheng¹(

),HU Zhenjie²,YE Lu³,ZHENG Yaru^4,*(

)

1. Department of Neurology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
2. Department of Respiratory and Critical Medicine, No. 906 Hospital of Chinese PLA, Ningbo 315040, China
3. Clinical Laboratory, Mental Health Center of Zhejiang University School of Medicine, Hangzhou Seventh People's Hospital, Hangzhou 310013, China
4. Department of Cardiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China

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摘要：

目的: 评价和比较Logistic回归和决策树分析用于预测急性心肌梗死（AMI）事件的可行性和有效性。方法: 回顾性分析2018年10月至2019年4月在浙江省人民医院因心绞痛或不明原因胸痛行选择性冠状动脉造影的295例患者的临床资料，其中55例诊断为AMI。分别利用Logistic回归分析和决策树分析建立AMI事件预测模型，并在是否根据Logistic回归结果条件下建立决策树分析模型（决策树1和决策树2），继而利用ROC曲线评估上述三组模型预测AMI的价值。结果: 二元Logistic回归分析结果显示，冠心病史、冠状动脉多支病变、他汀类药物史和载脂蛋白A1是AMI发生的独立影响因素（均P < 0.05）。不根据Logistic回归分析结果建立的决策树模型（决策树1）显示，冠状动脉多支病变为根节点，其后分别是冠心病史、载脂蛋白A1水平（以1.314 g/L作为分界点）和抗血小板聚集药物史作为子节点；而根据Logistic回归分析结果建立的决策树模型（决策树2）显示，冠状动脉多支病变为根节点，其后是冠心病史和载脂蛋白A1作为子节点。在对AMI事件的预测中，Logistic回归模型的AUC为0.826，而决策树模型的AUC分别为0.765（决策树1）和0.726（决策树2）。三组模型间比较结果显示，Logistic回归模型的AUC优于决策树2（95% CI：0.041~0.145，Z=3.534，P < 0.01），但与决策树1差异无统计学意义（95% CI：-0.014~0.121，Z=-1.173，P>0.05）。结论: 在对AMI事件的预测分析中，不根据Logistic回归模型结果建立的决策树模型效力与Logistic回归模型相当，未来有望应用于AMI患者的防治工作。

关键词： 心肌梗死; 急性病; Logistic模型; 回归分析; 决策树; 预测

Abstract:

Objective: To evaluate the application of decision tree method and Logistic regression in the prediction of acute myocardial infarction (AMI) events. Methods: The clinical data of 295 patients, who underwent coronary angiography due to angina or chest pain with unidentified causes in Zhejiang provincial People's Hospital during October 2018 and April 2019, were retrospectively analyzed. Fifty five patients were identified as AMI. Logistic regression and decision tree methods were performed to establish predictive models for the occurrence of AMI, respectively; and the models created by decision tree analysis were divided into Logistic regression-independent model (Tree 1) and Logistic regression-dependent model (Tree 2). The performance of Logistic regression and decision tree models were compared using the area under the receiver operating characteristic (ROC) curve. Results: Logistic regression analysis showed that history of coronary artery disease, multi-vessel coronary artery disease, statin use and apolipoprotein (ApoA1) level were independent influencing factors of AMI events (all P < 0.05). Logistic regression-independent decision tree model (Tree 1) showed that multi-vessel coronary artery disease was the root node, and history of coronary artery disease, ApoA1 level (the cutoff value:1.314 g/L) and anti-platelet drug use were descendant nodes. In Logistic regression-dependent decision tree model (Tree 2), multi-vessel coronary artery disease was still the root node, but only followed by two descendant nodes including history of coronary artery disease and ApoA1 level. The area under the curve (AUC) of ROC of Logistic regression model was 0.826, and AUCs of decision tree models were 0.765 and 0.726, respectively. AUC of Logistic regression model was significantly higher than that of Tree 2 (95% CI=0.041-0.145, Z=3.534, P < 0.001), but was not higher than that of Tree 1 (95% CI=-0.014-0.121, Z=-1.173, P>0.05). Conclusion: The predictive value for AMI event was comparable between Logistic regression-independent decision tree model and Logistic regression model, implying the data mining methods are feasible and effective in AMI prevention and control.

Key words: Myocardial infarction Acute disease Logistic models Regression analysis Decision trees Forecasting

收稿日期: 2019-06-05 出版日期: 2020-01-19

R542.2+2

基金资助: 国家自然科学基金(81801162);浙江省医学会临床科研资金项目(2017XYC-A02)

通讯作者: 郑亚如 E-mail: xiaoxiaoqing_23@hotmail.com;zhengyaru@zjheart.com

作者简介: 张圣(1986—), 女, 博士, 主治医师, 主要从事心脑血管疾病的临床研究; E-mail:xiaoxiaoqing_23@hotmail.com; https://orcid.org/0000-0003-0644-7930

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引用本文:

张圣,胡振杰,叶璐,郑亚如. 决策树分析在急性心肌梗死事件预测中的应用[J]. 浙江大学学报（医学版）, 2019, 48(6): 594-602.

ZHANG Sheng,HU Zhenjie,YE Lu,ZHENG Yaru. Application of Logistic regression and decision tree analysis in prediction of acute myocardial infarction events. J Zhejiang Univ (Med Sci), 2019, 48(6): 594-602.

链接本文:

http://www.zjujournals.com/med/CN/10.3785/j.issn.1008-9292.2019.12.02 或 http://www.zjujournals.com/med/CN/Y2019/V48/I6/594

表 1 发生AMI影响因素的单因素分析结果

表 2 预测急性心肌梗死事件的二元Logistic回归分析结果

图 1 不根据Logistic回归分析结果建立的预测AMI事件的决策树模型

图 2 Logistic回归和决策树模型预测AMI的ROC曲线

表 3 拆分数据集后进入决策树模型的变量

表 4 Logistic回归和决策树模型预测AMI的ROC曲线分析结果

表 5 预测冠状动脉多支病变的单因素分析结果

表 6 预测冠状动脉多支病变的二元Logistic回归分析结果

图 3 颈动脉最大斑块面积预测冠状动脉多支病变的ROC曲线

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