AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease Using Novel Non-Invasive Biomarkers
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Le résumé fourni par la source
Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum parameters for classifier or regression technique (regressor) were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel 'biomarkers-ML combination' approach to risk stratification.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease Using Novel Non-Invasive Biomarkers
- Date Crossref
- 01/12/2024
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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National University of Sciences and Technology Department of Computer and Software Engineering pays non établi dans la noticeUniversité ou école supérieure
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National University of Medical Sciences pays non établi dans la noticeUniversité ou école supérieure
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King Saud University pays non établi dans la noticeUniversité ou école supérieure
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Military College of Signals (MCS) Department of Software Engineering pays non établi dans la noticeUniversité ou école supérieure
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School of Electrical Engineering and Computer Science (SEECS) pays non établi dans la noticeUniversité ou école supérieure
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College of Computer and Information Sciences Department of Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
Department of Computer and Software Engineering — National University of Sciences and Technology, National University of Medical Sciences et King Saud University, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.