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An approach for auxiliary diagnosing and screening coronary disease based on machine learning

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Document pages: 3 pages

Abstract: How to accurately classify and predict whether an individual has CoronaryArtery Disease (CAD) and the degree of coronary stenosis without using invasiveexamination? This problem has not been solved satisfactorily. To this end, thethree kinds of machine learning (ML)algorithms, i.e., Boost Tree (BT), DecisionTree (DT), Logistic Regression (LR), are employed in this paper. First, 11features including basic information of an individual, symptoms and results ofroutine physical examination are selected, and one label is specified,indicating whether an individual suffers from CAD or different severity ofcoronary 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 obtainthe corresponding optimal predictive results, respectively. The experimentalresults show that: BT predicts whether an individual has CAD with an accuracyof 94 , and this algorithm predicts the degree of an individuals coronaryartery stenosis with an accuracy of 90 .

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