Benchmarking missing-values approaches for predictive models on health databases

TL;DR

This paper systematically benchmarks missing-value handling strategies for predictive models on large health databases, showing native support for missing values often outperforms imputation methods in accuracy and efficiency.

Contribution

It demonstrates that native missing-value support in gradient-boosted trees is superior to imputation in predictive accuracy and computational efficiency on large health datasets.

Findings

Native missing-value support outperforms imputation in accuracy.

Adding indicator variables improves imputation-based predictions.

Native support is faster and more robust than complex imputation methods.

Abstract

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.