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. 2012 Sep;54(5):657-73.
doi: 10.1002/bimj.201100251. Epub 2012 Jul 6.

Regression trees for predicting mortality in patients with cardiovascular disease: what improvement is achieved by using ensemble-based methods?

Peter C Austin  1 , Douglas S Lee, Ewout W Steyerberg, Jack V Tu
Affiliations
Free PMC article

Regression trees for predicting mortality in patients with cardiovascular disease: what improvement is achieved by using ensemble-based methods?

Peter C Austin et al. Biom J. 2012 Sep.
Free PMC article

Abstract

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999-2001 and 2004-2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease.

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Figures

Figure 1
Figure 1
Calibration plot in EFFECT2 AMIcohort.
Figure 2
Figure 2
Relationship between key continuous variables and log-odds of death.
Figure 3
Figure 3
Distribution of predicted probabilities of death in AMI sample.
Figure 4
Figure 4
Calibration plot in EFFECT2 CHF cohort.
See this image and copyright information in PMC

References

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