Electrocardiography Techniques for the Prediction and Detection of Coronary Artery Disease: Intelligent Diagnostic Support Systems

Authors

  • Fatima Alrawi Department of Intelligent Medical Systems, College of Biomedical Informatics, University of Information Technology and Communications, Baghdad, Iraq.
  • Farah Qahtan Department of Bioinformatics, College of Biomedical Informatics, University of Information Technology and Communications, Baghdad, Iraq.
  • Moatasim Amer Mohammed Department of Intelligent Medical Systems, College of Biomedical Informatics, University of Information Technology and Communications, Baghdad, Iraq.

The high mortality rate around the world can be attributed to Coronary Artery Disease (CAD). Therefore, in order to prevent extreme outcomes (including coronary heart attacks and sudden death), early detection is essential. A diagnostic (non-invasive) tool commonly used to identify early symptoms of CAD is Electrocardiography (ECG). However, the interpreter's knowledge may overlook diffuse patterns, thus affecting the accuracy. Artificial Intelligence (AI) is a promising solution that has emerged recently. That is because it can utilize advanced algorithms to analyze large ECG datasets and identify previously hidden indicators of early-stage disease. This research demonstrates the significance of ECG in CAD diagnosis and investigates how AI can enhance diagnostic accuracy to improve preventive measures for cardiac disease.

Keywords:

ECG, Coronary Artery Disease, AI

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Electrocardiography Techniques for the Prediction and Detection of Coronary Artery Disease: Intelligent Diagnostic Support Systems. (2026). Journal Port Science Research, 9(2), 371-376. https://doi.org/10.36371/mwp62126

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Electrocardiography Techniques for the Prediction and Detection of Coronary Artery Disease: Intelligent Diagnostic Support Systems. (2026). Journal Port Science Research, 9(2), 371-376. https://doi.org/10.36371/mwp62126