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 re...

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Veröffentlicht in:	Statistics in medicine 1998-11, Vol.17 (21), p.2501-2508
Hauptverfasser:	Ennis, Marguerite, Hinton, Geoffrey, Naylor, David, Revow, Mike, Tibshirani, Robert
Format:	Artikel
Sprache:	eng
Schlagworte:	Biological and medical sciences Computerized, statistical medical data processing and models in biomedicine Databases as Topic Humans Logistic Models Medical sciences Medical statistics Myocardial Infarction - drug therapy Myocardial Infarction - mortality Neural Networks (Computer) Risk Factors Survival Rate Thrombolytic Therapy
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Zusammenfassung:	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.
ISSN:	0277-6715 1097-0258
DOI:	10.1002/(SICI)1097-0258(19981115)17:21<2501::AID-SIM938>3.0.CO;2-M