Stable clinical prediction using graph support vector machines

Kamkar, Iman, Gupta, Sunil, Li, Cheng, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, Stable clinical prediction using graph support vector machines, in 2016 23rd International Conference on Pattern Recognition (ICPR 2016), IEEE, Piscataway, N.J., pp. 3332-3337, doi: 10.1109/ICPR.2016.7900148.

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Title Stable clinical prediction using graph support vector machines
Author(s) Kamkar, Iman
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Li, ChengORCID iD for Li, Cheng orcid.org/0000-0002-9977-8247
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0001-8675-6631
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Pattern Recognition. Conference (23rd : 2016 : Cancun, Mexico)
Conference location Cancun, Mexico
Conference dates 2016/12/04 - 2016/12/08
Title of proceedings 2016 23rd International Conference on Pattern Recognition (ICPR 2016)
Editor(s) [Unknown],
Publication date 2016
Start page 3332
End page 3337
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
REGRESSION SHRINKAGE
VARIABLE SELECTION
MODEL SELECTION
LASSO
Summary The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard similarity among features. Our method employs a convex function to penalize the pairwise l ∞ -norm of connected feature coefficients in the graph. We apply the alternating direction method of multipliers to solve the proposed formulation. Our experiments are conducted on a synthetic and three real-world hospital datasets. We show that our proposed method is more stable than the state-of-the-art feature selection and classification techniques in terms of three stability measures namely, Jaccard similarity measure, Spearman's rank correlation coefficient and Kuncheva index. We further show that our method has resulted in better classification performance compared to the baselines.
ISBN 9781509048472
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2016.7900148
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30098065

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