论文标题
使用机器学习诊断冠状动脉狭窄的辅助诊断
Auxiliary Diagnosing Coronary Stenosis Using Machine Learning
论文作者
论文摘要
如何准确地分类和诊断一个人是否患有冠状动脉狭窄（CS）而没有侵入性身体检查？这个问题尚未令人满意地解决。为此，本文采用了四种机器学习（ML）算法，即增强树（BT），决策树（DT），逻辑回归（LR）和随机森林（RF）。首先，选择了11个功能，包括个人的基本信息，常规体格检查的症状和结果，以及指定一个标签，表明一个人是否患有不同严重性的冠状动脉狭窄。基于它，构建了样本集。其次，这四种ML算法中的每一种都从样品集中学习，分别获得相应的最佳分类结果。实验结果表明：RF的性能优于其他三种算法，并且前者算法对个人的CS是否精度为95.7％（= 90/94）进行了分类。
How to accurately classify and diagnose whether an individual has Coronary Stenosis (CS) without invasive physical examination? This problem has not been solved satisfactorily. To this end, the four machine learning (ML) algorithms, i.e., Boosted Tree (BT), Decision Tree (DT), Logistic Regression (LR) and Random Forest (RF) are employed in this paper. First, eleven features including basic information of an individual, symptoms and results of routine physical examination are selected, as well as one label is specified, indicating whether an individual suffers from different severity of coronary artery stenosis or not. On the basis of it, a sample set is constructed. Second, each of these four ML algorithms learns from the sample set to obtain the corresponding optimal classified results, respectively. The experimental results show that: RF performs better than other three algorithms, and the former algorithm classifies whether an individual has CS with an accuracy of 95.7% (=90/94).
