Auxiliary Diagnosing Coronary Stenosis Using Machine Learning
This work addresses the need for accurate non-invasive diagnosis of coronary stenosis in medical patients, but it is incremental as it applies standard machine learning methods without novel contributions.
The paper tackled the problem of non-invasive diagnosis of coronary stenosis by applying four machine learning algorithms to a dataset of 11 features, finding that Random Forest achieved the highest accuracy of 95.7%.
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).