Model uncertainty quantification for diagnosis of each main coronary artery stenosis

Alizadehsani, Roohallah, Roshanzamir, Mohamad, Abdar, Moloud, Beykikhoshk, Adham, Zangooei, Mohammad Hossein, Khosravi, Abbas, Nahavandi, Saeid, Tan, Ru San and Acharya, U. Rajendra 2019, Model uncertainty quantification for diagnosis of each main coronary artery stenosis, Soft Computing, pp. 1-12, doi: 10.1007/s00500-019-04531-0.

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Title Model uncertainty quantification for diagnosis of each main coronary artery stenosis
Author(s) Alizadehsani, Roohallah
Roshanzamir, Mohamad
Abdar, Moloud
Beykikhoshk, Adham
Zangooei, Mohammad Hossein
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Tan, Ru San
Acharya, U. Rajendra
Journal name Soft Computing
Start page 1
End page 12
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Publication date 2019
ISSN 1432-7643
1433-7479
Keyword(s) Data mining
Machine learning
Coronary artery disease
SVM
Gini index
LAD
Feature selection
Notes In press article
Language eng
DOI 10.1007/s00500-019-04531-0
Indigenous content off
Field of Research 0102 Applied Mathematics
0801 Artificial Intelligence and Image Processing
1702 Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, Springer-Verlag GmbH Germany, part of Springer Nature.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30133093

Document type: Journal Article
Collection: Institute for Intelligent Systems Research and Innovation (IISRI)
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