Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study.

Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study.

<h4>Background</h4>Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomogra...

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Main Authors: Lohendran Baskaran, Xiaohan Ying, Zhuoran Xu, Subhi J Al'Aref, Benjamin C Lee, Sang-Eun Lee, Ibrahim Danad, Hyung-Bok Park, Ravi Bathina, Andrea Baggiano, Virginia Beltrama, Rodrigo Cerci, Eui-Young Choi, Jung-Hyun Choi, So-Yeon Choi, Jason Cole, Joon-Hyung Doh, Sang-Jin Ha, Ae-Young Her, Cezary Kepka, Jang-Young Kim, Jin-Won Kim, Sang-Wook Kim, Woong Kim, Yao Lu, Amit Kumar, Ran Heo, Ji Hyun Lee, Ji-Min Sung, Uma Valeti, Daniele Andreini, Gianluca Pontone, Donghee Han, Todd C Villines, Fay Lin, Hyuk-Jae Chang, James K Min, Leslee J Shaw
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0233791

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https://doi.org/10.1371/journal.pone.0233791

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