Am J Cardiovasc Dis 2011;1(1):1-15
Original Article
Boosted classification trees result in minor to modest improvement in the
accuracy in classifying cardiovascular outcomes compared to conventional
classification trees
Peter C. Austin, Douglas S. Lee
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Department of Health Management, Policy and
Evaluation, University of Toronto; Dalla Lana School of Public Health, University of Toronto; Department of Medicine,
University Health Network and Faculty of Medicine, University of Toronto, Toronto, Canada
Received March 22, 2011; accepted April 20, 2011; Epub April 23, 2011; published June 1, 2011
Abstract: Purpose: Classification trees are increasingly being to classifying patients according to the presence or absence
of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining
and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees
iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were
misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications
from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final
classification. The authors‟ objective was to examine whether boosting improved the accuracy of classification trees for
predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for
classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive
heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor
compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were
observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees
had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes
were less impressive than gains in performance observed in the data mining literature. (AJCD1103001).
Keywords: Boosting, classification trees, predictive model, classification, recursive partitioning, congestive heart failure,
acute myocardial infarction, outcomes research
Full Text PDF
Address all correspondence to:
Peter C. Austin, PhD
Institute for Clinical Evaluative Sciences
G1 06, 2075 Bayview Avenue
Toronto, Ontario
M4N 3M5
Canada.
Phone: (416) 480-6131 Fax: (416) 480-6048
E-mail: peter.austin@ices.on.ca
Original Article
Boosted classification trees result in minor to modest improvement in the
accuracy in classifying cardiovascular outcomes compared to conventional
classification trees
Peter C. Austin, Douglas S. Lee
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Department of Health Management, Policy and
Evaluation, University of Toronto; Dalla Lana School of Public Health, University of Toronto; Department of Medicine,
University Health Network and Faculty of Medicine, University of Toronto, Toronto, Canada
Received March 22, 2011; accepted April 20, 2011; Epub April 23, 2011; published June 1, 2011
Abstract: Purpose: Classification trees are increasingly being to classifying patients according to the presence or absence
of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining
and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees
iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were
misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications
from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final
classification. The authors‟ objective was to examine whether boosting improved the accuracy of classification trees for
predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for
classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive
heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor
compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were
observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees
had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes
were less impressive than gains in performance observed in the data mining literature. (AJCD1103001).
Keywords: Boosting, classification trees, predictive model, classification, recursive partitioning, congestive heart failure,
acute myocardial infarction, outcomes research
Full Text PDF
Address all correspondence to:
Peter C. Austin, PhD
Institute for Clinical Evaluative Sciences
G1 06, 2075 Bayview Avenue
Toronto, Ontario
M4N 3M5
Canada.
Phone: (416) 480-6131 Fax: (416) 480-6048
E-mail: peter.austin@ices.on.ca
 |  |  |  |  |  |  |
| AJCD Copyright © 2011-present, All rights reserved. Published by e-Century Publishing Corporation, Madison, WI 53711, USA |