Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?
Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?
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DOI：
10.1002/bimj.201100251
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发表时间：
2012-09-01
影响因子：
1.7
通讯作者：
Tu, Jack V.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.;Lee, Douglas S.;Tu, Jack V.
在生物医学研究中，逻辑回归模型是预测二元结果概率最常用的方法。虽然许多临床研究人员对回归树表示了热情，但这种方法在预测健康结果方面的准确性可能有限。我们的目标是评估通过使用基于集成的方法所实现的改进，包括回归树、随机森林和增强回归树的自举聚合（bagging）。我们分析了两个不同时期（1999 - 2001年和2004 - 2005年）急性心肌梗死（N = 16,230）或充血性心力衰竭（N = 15,848）住院患者的30天死亡率。我们发现，与传统的回归树相比，集合方法的样本内和样本外预测在预测心血管死亡率方面都有很大的改进。然而，传统的包含受限三次平滑样条的逻辑回归模型具有更好的性能。我们的结论是，来自数据挖掘和机器学习文献的集成方法提高了回归树的预测性能，但在预测心血管疾病患者群体样本的短期死亡率方面，可能不会比传统的逻辑回归模型有明显的优势。
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 (19992001 and 20042005). 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.