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

DOI10.1002/bimj.201100251zbMath1400.62244OpenAlexW1926248049WikidataQ36315061 ScholiaQ36315061MaRDI QIDQ2919467

Jack V. Tu, Douglas S. Lee, Ewout W. Steyerberg, Peter C. Austin

Publication date: 2 October 2012

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201100251

zbMATH Keywords

data miningbaggingboostingheart failureacute myocardial infarction

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Application of random forest survival models to increase generalizability of decision trees: a case study in acute myocardial infarction ⋮ Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory ⋮ Risk prediction with machine learning and regression methods

Uses Software

• ElemStatLearn

Cites Work

• Boosting algorithms: regularization, prediction and model fitting
• Clinical prediction models. A practical approach to development, validation, and updating.
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Regression modeling strategies. With applications to linear models, logistic regression and survival analysis
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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