Towards an Explainable AI-based Tool to Predict the Presence of Obstructive Coronary Artery Disease-Reference-Cited by-同舟云学术

Towards an Explainable AI-based Tool to Predict the Presence of Obstructive Coronary Artery Disease

Published:2022-11-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th Pan-Hellenic Conference on Informatics
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Author:

Kyparissidis Kokkinidis Ilias¹^ORCID,Rigas Emmanouil S.¹^ORCID,Logaras Evangelos¹^ORCID,Samaras Athanasios¹^ORCID,Rampidis Georgios P.¹^ORCID,Giannakoulas George¹^ORCID,Kouskouras Konstantinos¹^ORCID,Billis Antonios¹^ORCID,Bamidis Panagiotis D.¹^ORCID

Affiliation:

1. Aristotle University of Thessaloniki, Greece

Funder

Horizon 2020 Framework Programme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3575879.3576014

Reference18 articles.

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2. Saba Bashir Usman Qamar and Farhan Hassan Khan. 2015. BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australasian physical & engineering sciences in medicine 38 2(2015) 305–323. Saba Bashir Usman Qamar and Farhan Hassan Khan. 2015. BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australasian physical & engineering sciences in medicine 38 2(2015) 305–323.

3. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve

4. Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism—a non-cardiac cause of cardiac arrest

5. Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis

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