Auxiliary Diagnosing Coronary Stenosis Using Machine Learning

TL;DR

This paper explores the use of four machine learning algorithms to non-invasively diagnose coronary stenosis, with Random Forest achieving the highest accuracy of 95.7%.

Contribution

It compares four ML algorithms for diagnosing coronary stenosis using routine clinical features, highlighting the superior performance of Random Forest.

Findings

Random Forest outperforms other algorithms in accuracy.

Achieved 95.7% accuracy in classifying coronary stenosis.

Routine features can effectively predict CS.

Abstract

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).