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.
|Title of host publication||2020 Computing in Cardiology, CinC 2020|
|Place of Publication||Rimini, Italy|
|Publication status||Published - 10 Feb 2021|
|Event||Computing in Cardiology 2020 - Palacongressi, Rimini, Italy|
Duration: 13 Sep 2020 → 16 Sep 2020
|Name||2020 COMPUTING IN CARDIOLOGY|
|Conference||Computing in Cardiology 2020|
|Period||13/09/20 → 16/09/20|
© 2020 Creative Commons; the authors hold their copyright.
- Acute Coronary Syndrome
- Machine Learning
- Biomarker discovery