A comparison of statistical learning methods on the Gusto database

Stat Med. 1998 Nov 15;17(21):2501-8. doi: 10.1002/(sici)1097-0258(19981115)17:21<2501::aid-sim938>3.0.co;2-m.


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.

Publication types

  • Comparative Study

MeSH terms

  • Databases as Topic*
  • Humans
  • Logistic Models*
  • Myocardial Infarction / drug therapy*
  • Myocardial Infarction / mortality*
  • Neural Networks, Computer*
  • Risk Factors
  • Survival Rate
  • Thrombolytic Therapy*