Paper Info
Title
Improving The Detection Of Acute Coronary Syndrome Using Machine Learning Of Blood Biomarkers
Abstract
Background: Acute coronary syndrome (ACS) is one of the main causes of death worldwide. The 12-lead electrocardiogram (ECG) is used to help diagnose ACS, along with clinical risk factors (smoking, diabetes mellitus, hypertension, hscTn and positive family history of ACS. These methods however are associated with many limitations resulting in variable sensitivity/specificity. The aim of this study was to use a machine learning approach to develop an optimum panel of blood protein biomarkers capable of independently diagnosing ACS. Methods: A hybrid feature selection and ML prediction algorithms including two classifiers: 1) decision tree (DT) and 2) logistic regression were applied to protein biomarkers (327 proteins) collected from patients with ACS n=91 or non-ACS n=97. Results: Using this approach, 20 proteins out of 327 proteins were able to accurately distinguish between ACS and non-ACS (logistic regression ROC-AUC=0.8 and accuracy=82.5% and DT ROC-AUC=0.6 and accuracy=64.9%. Conclusion: Logistic regression obtained a higher performance compared to DT and showed promising results uncovering a panel of 20 protein biomarkers which included those associated with progressive atherosclerotic plaques, myocardial injury and inflammation. This approach was able to accurately discriminate between patients with ACS and non-ACS.
Year
DOI
Venue
2020
10.22489/CinC.2020.337
2020 COMPUTING IN CARDIOLOGY
DocType
ISSN
Citations 
Conference
2325-8861
0
PageRank 
References 
Authors
0.34
0
13
Name
Order
Citations
PageRank
Khaled Rjoob103.04
Victoria McGilligan200.34
Raymond Bond300.34
Steven Watterson400.34
Melody Chemaly500.34
Roisin McAlister600.34
Tiago De Melo Malaquias700.34
Stephen J. Leslie800.34
Charles Knoery901.69
Aleeha Iftikhar1001.69
Anne McShane1101.35
Anthony Bjourson1200.34
Aaron J. Peace1301.01