Paper Info
Title
Site Specific Prediction Of Pci Stenting Based On Imaging And Biomechanics Data Using Gradient Boosting Tree Ensembles
Abstract
Cardiovascular diseases are nowadays considered as the main cause of morbidity and mortality worldwide. Coronary Artery Disease (CAD), the most typical form of cardiovascular disease is diagnosed by a variety of imaging modalities, both invasive and non-invasive, which involve either risk implications or high cost. Therefore, several attempts have been undertaken to early diagnose and predict either the high CAD risk patients or the cardiovascular events, implementing machine learning techniques. The purpose of this study is to present a classification scheme for the prediction of Percutaneous Coronary Intervention (PCI) stenting placement, using image-based data. The proposed classification model is a gradient boosting classifier, incorporated into a class imbalance handling technique, the Easy ensemble scheme and aims to classify coronary segments into high CAD risk and low CAD risk, based on their PCI placement. Through this study, we investigate the importance of image based features, concluding that the combination of the coronary degree of stenosis and the fractional flow reserve achieves accuracy 78%.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9175612
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
11
Name
Order
Citations
PageRank
Vassiliki I Kigka100.34
Antonis Sakellarios23218.83
Eleni I. Georga3306.48
Panagiotis K Siogkas42010.49
Panagiota Tsompou512.17
Savvas Kyriakidis601.01
Silvia Rocchiccioli702.37
Gualtiero Pelosi855.47
Katerina K. Naka9207.99
Lampros K. Michalis1017721.89
Dimitrios I. Fotiadis11941121.32