PCA-based Polling Strategy in Machine Learning Framework for Coronary Artery Disease Risk Assessment in Intravascular Ultrasound: A Link between Carotid and Coronary Grayscale Plaque Morphology

Highlights • Coronary artery disease risk assessment in intravascular ultrasound. • A link between carotid and coronary grayscale plaque morphology. • Principal component analysis (PCA) for dominant feature selection. • Classification accuracy of 98.43% and reliability index of 97.32%.

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Veröffentlicht in:	Computer methods and programs in biomedicine 2016-05, Vol.128, p.137-158
Hauptverfasser:	Araki, Tadashi, MD, Ikeda, Nobutaka, MD, Shukla, Devarshi, BTech, Jain, Pankaj K., B.E, Londhe, Narendra D., PhD, Shrivastava, Vimal K., M.Tech, Banchhor, Sumit K., MTech, Saba, Luca, MD, Nicolaides, Andrew, PhD, Shafique, Shoaib, MD, Laird, John R., MD, Suri, Jasjit S., PhD, MBA, Fellow AIMBE
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Aged, 80 and over Algorithms Carotid Arteries - diagnostic imaging Carotid IMT Carotid Intima-Media Thickness Computational Biology - methods Computer-Aided Design Coronary artery Coronary artery disease Coronary Artery Disease - diagnostic imaging Coronary Vessels - diagnostic imaging Female Humans Internal Medicine IVUS Machine Learning Male Middle Aged Morphology Other PCA Plaque, Atherosclerotic - diagnostic imaging Principal Component Analysis - methods Reproducibility of Results Retrospective Studies Risk assessment Risk Assessment - methods Stability Strategy Stratification Support Vector Machine Support vector machines Ultrasonography
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Beschreibung
Zusammenfassung:	Highlights • Coronary artery disease risk assessment in intravascular ultrasound. • A link between carotid and coronary grayscale plaque morphology. • Principal component analysis (PCA) for dominant feature selection. • Classification accuracy of 98.43% and reliability index of 97.32%.
ISSN:	0169-2607 1872-7565
DOI:	10.1016/j.cmpb.2016.02.004