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.
Journal:
Computer methods and programs in biomedicine
Published Date:
May 1, 2016
Abstract
BACKGROUND AND OBJECTIVE: Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup.
Authors
Keywords
Adult
Aged
Aged, 80 and over
Algorithms
Carotid Arteries
Carotid Intima-Media Thickness
Computational Biology
Computer-Aided Design
Coronary Artery Disease
Coronary Vessels
Female
Humans
Machine Learning
Male
Middle Aged
Plaque, Atherosclerotic
Principal Component Analysis
Reproducibility of Results
Retrospective Studies
Risk Assessment
Support Vector Machine
Ultrasonography