Conference item
Risk prediction for cardiovascular disease using ECG data in the China Kadoorie Biobank
- Abstract:
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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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Access Document
- Files:
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(Accepted manuscript, pdf, 737.9KB)
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- Publisher copy:
- 10.1109/EMBC.2016.7591218
Authors
Funding
+ Engineering and Physical Sciences Research Council
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Grant:
Digital Economy Programme grant EP/G036861/1
Royal Academy of Engineering
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China Scholarship Council
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Bibliographic Details
- 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:
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624287
Item Description
- Pubs id:
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pubs:624287
- UUID:
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uuid:2f6e4f59-9b0f-4758-b917-a000c7e04869
- Local pid:
- pubs:624287
- Deposit date:
- 2016-05-27
Terms of use
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2016
- Notes:
- ©2016 IEEE.
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