Coronary Artery Disease Detection by Machine Learning with Coronary Bifurcation Features

被引:13
|
作者
Chen, Xueping [1 ]
Fu, Yi [2 ,3 ]
Lin, Jiangguo [1 ]
Ji, Yanru [1 ]
Fang, Ying [1 ]
Wu, Jianhua [1 ]
机构
[1] South China Univ Technol, Sch Biosci & Bioengn, Inst Biomech, Guangzhou 510006, Peoples R China
[2] Shanghai Univ Med & Hlth Sci, Collaborat Innovat Ctr Biomed, Shanghai 200237, Peoples R China
[3] Shanghai Univ Med & Hlth Sci, Sch Med Instruments, Shanghai 200237, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 21期
基金
中国国家自然科学基金;
关键词
coronary artery disease; coronary bifurcations; machine learning; morphological features; classification performance; SUPPORT VECTOR MACHINES; CLASSIFICATION; DIAGNOSIS;
D O I
10.3390/app10217656
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Background: Early accurate detection of coronary artery disease (CAD) is one of the most important medical research areas. Researchers are motivated to utilize machine learning techniques for quick and accurate detection of CAD. Methods: To obtain the high quality of features used for machine learning, we here extracted the coronary bifurcation features from the coronary computed tomography angiography (CCTA) images by using the morphometric method. The machine learning classifier algorithms, such as logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), artificial neural network (ANN), and support vector machine (SVM) were applied for estimating the performance by using the measured features. Results: The results showed that in comparison with other machine learning methods, the polynomial-SVM with the use of the grid search optimization method had the best performance for the detection of CAD and had yielded the classification accuracy of 100.00%. Among six examined coronary bifurcation features, the exponent of vessel diameter (n) and the area expansion ratio (AER) were two key features in the detection of CAD. Conclusions: This study could aid the clinicians to detect CAD accurately, which may probably provide an alternative method for the non-invasive diagnosis in clinical.
引用
收藏
页码:1 / 18
页数:18
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