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A comparison of statistical learning methods on the GUSTO database

A comparison of statistical learning methods on the GUSTO database We apply a battery of modern, adaptive non‐linear learning methods to a large real database of cardiac patient data. We use each method to predict 30 day mortality from a large number of potential risk factors, and we compare their performances. We find that none of the methods could outperform a relatively simple logistic regression model previously developed for this problem. © 1998 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

A comparison of statistical learning methods on the GUSTO database

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References (15)

Publisher
Wiley
Copyright
Copyright © 1998 John Wiley & Sons, Ltd.
ISSN
0277-6715
eISSN
1097-0258
DOI
10.1002/(SICI)1097-0258(19981115)17:21<2501::AID-SIM938>3.0.CO;2-M
Publisher site
See Article on Publisher Site

Abstract

We apply a battery of modern, adaptive non‐linear learning methods to a large real database of cardiac patient data. We use each method to predict 30 day mortality from a large number of potential risk factors, and we compare their performances. We find that none of the methods could outperform a relatively simple logistic regression model previously developed for this problem. © 1998 John Wiley & Sons, Ltd.

Journal

Statistics in MedicineWiley

Published: Nov 15, 1998

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