# Benchmarking missing-values approaches for predictive models on health databases

arXiv: https://arxiv.org/abs/2202.10580 · HTML: https://mlsanity.com/paper/2202.10580/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [On the consistency of supervised learning with missing values](https://mlsanity.com/paper/1902.06931/) (2019)
- [Linear predictor on linearly-generated data with missing values: non consistency and solutions](https://mlsanity.com/paper/2002.00658/) (2020)
- [Data-driven Advice for Applying Machine Learning to Bioinformatics Problems](https://mlsanity.com/paper/1708.05070/) (2017)
- [Missing Data Imputation for Supervised Learning](https://mlsanity.com/paper/1610.09075/) (2016)
- [Bootstrapping and Multiple Imputation Ensemble Approaches for Missing Data](https://mlsanity.com/paper/1802.00154/) (2018)

## Built on by (most cited first, 23 of 23)

- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [HyperImpute: Generalized Iterative Imputation with Automatic Model Selection](https://mlsanity.com/paper/2206.07769/) (2022)
- [The Missing Indicator Method: From Low to High Dimensions](https://mlsanity.com/paper/2211.09259/) (2022)
- [Imputation for prediction: beware of diminishing returns](https://mlsanity.com/paper/2407.19804/) (2024)
- [Not Another Imputation Method: A Transformer-based Model for Missing Values in Tabular Datasets](https://mlsanity.com/paper/2407.11540/) (2024)
- [Random features models: a way to study the success of naive imputation](https://mlsanity.com/paper/2402.03839/) (2024)
- [Adapting Fairness Interventions to Missing Values](https://mlsanity.com/paper/2305.19429/) (2023)
- [MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data](https://mlsanity.com/paper/2412.14810/) (2024)
- [No imputation without representation](https://mlsanity.com/paper/2206.14254/) (2022)
- [Robust prediction under missingness shifts](https://mlsanity.com/paper/2406.16484/) (2024)
- [missForestPredict -- Missing data imputation for prediction settings](https://mlsanity.com/paper/2407.03379/) (2024)
- [Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches](https://mlsanity.com/paper/2506.01696/) (2025)
- [Estimating treatment effects from single-arm trials via latent-variable modeling](https://mlsanity.com/paper/2311.03002/) (2023)
- [Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer](https://mlsanity.com/paper/2504.14610/) (2025)
- [A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations](https://mlsanity.com/paper/2507.22919/) (2025)
- [Polar Encoding: A Simple Baseline Approach for Classification with Missing Values](https://mlsanity.com/paper/2210.01905/) (2022)
- [Une comparaison des algorithmes d'apprentissage pour la survie avec donn'ees manquantes](https://mlsanity.com/paper/2303.13590/) (2023)
- [Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices](https://mlsanity.com/paper/2410.23503/) (2024)
- [Optimal Transport with Heterogeneously Missing Data](https://mlsanity.com/paper/2505.17291/) (2025)
- [Closing Gaps: An Imputation Analysis of ICU Vital Signs](https://mlsanity.com/paper/2510.24217/) (2025)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy](https://mlsanity.com/paper/2605.25050/) (2026)
- [CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping](https://mlsanity.com/paper/2606.29907/) (2026)

## Abstract (arXiv)

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.