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Conference item

Risk prediction for cardiovascular disease using ECG data in the China Kadoorie Biobank

Abstract:

We set out to use machine learning techniques to analyse ECG data to improve risk evaluation of cardiovascular disease in a very large cohort study of the Chinese population. We performed this investigation by (i) detecting “abnormality” using 3 one-class classification methods, and (ii) predicting probabilities of “normality”, arrhythmia, ischemia, and hypertrophy using a multiclass approach. For one-class classification, we considered 5 possible definitions for “normality” and used 10 autom...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/EMBC.2016.7591218

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Sub department:
Population Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Sub department:
Population Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Sub department:
Population Health
Role:
Author
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Grant:
Digital Economy Programme grant EP/G036861/1
Royal Academy of Engineering More from this funder
China Scholarship Council More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
EMBC 16: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Journal website
Host title:
EMBC 16: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publication date:
2016-10-01
Acceptance date:
2016-05-07
DOI:
Source identifiers:
624287
Pubs id:
pubs:624287
UUID:
uuid:2f6e4f59-9b0f-4758-b917-a000c7e04869
Local pid:
pubs:624287
Deposit date:
2016-05-27

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