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 |
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| container_end_page | 2508 |
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| container_issue | 21 |
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| container_title | Statistics in medicine |
| container_volume | 17 |
| creator | Ennis, Marguerite Hinton, Geoffrey Naylor, David Revow, Mike Tibshirani, Robert |
| description | 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. |
| doi_str_mv | 10.1002/(SICI)1097-0258(19981115)17:21<2501::AID-SIM938>3.0.CO;2-M |
| format | Article |
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| identifier | ISSN: 0277-6715 |
| ispartof | Statistics in medicine, 1998-11, Vol.17 (21), p.2501-2508 |
| issn | 0277-6715 1097-0258 |
| language | eng |
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| source | MEDLINE; Wiley Online Library All Journals |
| subjects | 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 |
| title | A comparison of statistical learning methods on the GUSTO database |
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