An approach for auxiliary diagnosing and screening coronary disease based on machine learning
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How to accurately classify and predict whether an individual has coronary disease and the degree of coronary stenosis without using invasive examination? This problem has not been solved satisfactorily. To this end, the three kinds of machine learning (ML) algorithms, i.e., Boost Tree (BT), Decision Tree (DT), Logistic Regression (LR), are employed in this paper. First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from coronary disease or different severity of coronary artery stenosis. On the basis of it, a sample set is constructed. Second, each of these three ML algorithms learns from the sample set to obtain the corresponding optimal predictive results, respectively. The experimental results show that: BT predicts whether an individual has coronary disease with an accuracy of 94 coronary artery stenosis with an accuracy of 90
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