title: A comparison of statistical learning methods on the Gusto database.
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.
authors.affiliation: Department of Statistics, University of Toronto, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Ennis
authors.forename: G
authors.initials: G
authors.lastname: Hinton
authors.forename: D
authors.initials: D
authors.lastname: Naylor
authors.forename: M
authors.initials: M
authors.lastname: Revow
authors.forename: R
authors.initials: R
authors.lastname: Tibshirani
mesh_terms: 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