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.
Ennis
M
M
Department of Statistics, University of Toronto, Canada.
Hinton
G
G
Naylor
D
D
Revow
M
M
Tibshirani
R
R
D019992:Databases as Topic; D006801:Humans; D016015:Logistic Models; D009203:Myocardial Infarction / Q000188:drug therapy* / Q000401:mortality*; D016571:Neural Networks, Computer; D012307:Risk Factors; D015996:Survival Rate; D015912:Thrombolytic Therapy