摘要
本文旨在构建基于临床电子病历数据的冠心病预测模型.回顾性收集了2015年至2020年在宁波大学医学院附属医院住院期间,接受选择性冠状动脉造影的患者的临床数据,分别应用决策树、朴素贝叶斯和逻辑回归算法构建冠心病预测模型,比较3种模型的预测性能.共收集354例患者数据,其中冠心病患者140例,非冠心病患者214例,根据逻辑回归、朴素贝叶斯、决策树算法构建的3种预测模型的准确性分别为70.6%、89.5%、90.7%;曲线下面积分别为0.676、0.869、0.921.所构建的3种预测模型均具备较好的冠心病预测能力,具有用于冠心病筛查的潜在价值.
The current study was designed to construct coronary heart disease(CHD)prediction models based on clinical electronic medical record data.The clinical data was collected from the patients who underwent elective coronary angiography during hospitalization at the Affiliated Hospital of Medical School,Ningbo University from 2015 till 2020.Decision tree,naive Bayes,and logistic regression algorithms were applied to construct prediction models for coronary heart disease.The predictive performance of these three models was compared.A total of 354 patients,including 140 CHD patients and 214 non-CHD patients,were recruited.Using logistic regression,naive Bayes,and decision tree algorithms,the accuracies of the three prediction models were 70.6%,89.5%and 90.7%respectively.The AUCs were 0.676,0.869,0.921,respectively.All the three prediction models had good predictive ability for CHD and may have screening potential for CHD.
作者
陆浩轩
徐瑾妍
程可爱
谢燕青
王丽
计礼丽
周忠
杨卓
景胜
何文明
LU Haoxuan;XU Jinyan;CHENG Ke’ai;XIE Yanqing;WANG Li;JI Lili;ZHOU Zhong;YANG Zhuo;JING Sheng;HE Wenming(Department of Cardiology,Affiliated Hospital of Medical School,Ningbo University,Ningbo 315020,China;Department of Neurology,Affiliated Hospital of Medical School,Ningbo University,Ningbo 315020,China)
出处
《宁波大学学报（理工版）》
CAS
2022年第3期57-62,共6页
Journal of Ningbo University：Natural Science and Engineering Edition
基金
宁波市自然科学基金(2021J240,202003N4231)
宁波大学人体生物力学研究院开放基金(CJ-HBIO202104)。
关键词
冠心病
机器学习
决策树
朴素贝叶斯
coronary heart disease
machine learning
decision tree
naive Bayes
