Improving the Detection of Acute Coronary Syndrome Using Machine Learning of Blood Biomarkers

Khaled Rjoob, V. E. McGilligan, RR Bond, Steven Watterson, Melody El Chemaly, Roisin Mc Allister, Tiago De Melo Malaquias, Stephen James Leslie, Charles Knoery, Aleeha Iftikhar, Anne McShane, AJ Bjourson, Aaron Peace

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publication2020 Computing in Cardiology, CinC 2020
Place of PublicationRimini, Italy
ISBN (Electronic)978-1-7281-7382-5
DOIs
Publication statusPublished - 10 Feb 2021
EventComputing in Cardiology 2020 - Palacongressi, Rimini, Italy
Duration: 13 Sep 202016 Sep 2020

Publication series

NameComputing in Cardiology
Volume2020-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

ConferenceComputing in Cardiology 2020
Abbreviated titleCinC20
CountryItaly
CityRimini
Period13/09/2016/09/20

Keywords

  • Acute Coronary Syndrome
  • Machine Learning
  • Biomarker discovery

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