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Abstract
This paper investigates the use of machine learning (ML) to improve the diagnosis of functionally relevant coronary artery disease (fCAD). Two ML approaches are presented: one using eight static clinical variables and the other leveraging electrocardiogram (ECG) signals from exercise stress testing. Results show that ML outperforms cardiologists in predicting fCAD (AUROC: 0.71 vs. 0.64, p = 4.0E-13), potentially reducing imaging procedures by 15–17%. Combining ML with cardiologist judgment further improves performance (AUROC: 0.74).
Publisher
Nature Communications
Published On
Jun 12, 2024
Authors
Christian Bock, Joan Elias Walter, Bastian Rieck, Ivo Strebel, Klara Rumora, Ibrahim Schaefer, Michael J. Zellweger, Karsten Borgwardt, Christian Müller
Tags
machine learning
coronary artery disease
diagnosis
electrocardiogram
cardiology
healthcare

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