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Stable clinical prediction using graph support vector machines

conference contribution
posted on 2016-01-01, 00:00 authored by Iman Kamkar, Sunil GuptaSunil Gupta, Cheng Li, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh
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.

History

Event

Pattern Recognition. Conference (23rd : 2016 : Cancun, Mexico)

Pagination

3332 - 3337

Publisher

IEEE

Location

Cancun, Mexico

Place of publication

Piscataway, N.J.

Start date

2016-12-04

End date

2016-12-08

ISSN

1051-4651

ISBN-13

9781509048472

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2016, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

2016 23rd International Conference on Pattern Recognition (ICPR 2016)