BACKGROUND: As databases grow larger, it becomes harder to fully control
their collection, and they frequently come with missing values: incomplete
observations. These large databases are well suited to train machine-learning
models, for instance for forecasting or to extract biomarkers in biomedical
settings. Such predictive approaches can use discriminative -- rather than
generative -- modeling, and thus open the door to new missing-values
strategies. Yet existing empirical evaluations of strategies to handle missing
values have focused on inferential statistics. RESULTS: Here we conduct a
systematic benchmark of missing-values strategies in predictive models with a
focus on large health databases: four electronic health record datasets, a
population brain imaging one, a health survey and two intensive care ones.
Using gradient-boosted trees, we compare native support for missing values with
simple and state-of-the-art imputation prior to learning. We investigate
prediction accuracy and computational time. For prediction after imputation, we
find that adding an indicator to express which values have been imputed is
important, suggesting that the data are missing not at random. Elaborate
missing values imputation can improve prediction compared to simple strategies
but requires longer computational time on large data. Learning trees that model
missing values-with missing incorporated attribute-leads to robust, fast, and
well-performing predictive modeling. CONCLUSIONS: Native support for missing
values in supervised machine learning predicts better than state-of-the-art
imputation with much less computational cost. When using imputation, it is
important to add indicator columns expressing which values have been imputed.

本文系统评估了处理缺失值的策略在预测模型中的效果，重点关注大型健康数据库，结果表明，在监督式机器学习中自然支持的缺失值预测表现优于最先进的插补方法，且计算成本更低。