# On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets

arXiv: https://arxiv.org/abs/2403.14687 · HTML: https://mlsanity.com/paper/2403.14687/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology](https://mlsanity.com/paper/1809.03006/) (2018)
- [Imputation techniques on missing values in breast cancer treatment and fertility data](https://mlsanity.com/paper/2011.09912/) (2020)

## Built on by (most cited first, 3 of 3)

- [Precision Adaptive Imputation Network : An Unified Technique for Mixed Datasets](https://mlsanity.com/paper/2501.10667/) (2025)
- [Predicting Fetal Birthweight from High Dimensional Data using Advanced Machine Learning](https://mlsanity.com/paper/2502.14270/) (2025)
- [Ocular-Induced Abnormal Head Posture: Diagnosis and Missing Data Imputation](https://mlsanity.com/paper/2510.05649/) (2025)

## Abstract (arXiv)

Missing values or data is one popular characteristic of real-world datasets, especially healthcare data. This could be frustrating when using machine learning algorithms on such datasets, simply because most machine learning models perform poorly in the presence of missing values. The aim of this study is to compare the performance of seven imputation techniques, namely Mean imputation, Median Imputation, Last Observation carried Forward (LOCF) imputation, K-Nearest Neighbor (KNN) imputation, Interpolation imputation, Missforest imputation, and Multiple imputation by Chained Equations (MICE), on three healthcare datasets. Some percentage of missing values - 10%, 15%, 20% and 25% - were introduced into the dataset, and the imputation techniques were employed to impute these missing values. The comparison of their performance was evaluated by using root mean squared error (RMSE) and mean absolute error (MAE). The results show that Missforest imputation performs the best followed by MICE imputation. Additionally, we try to determine whether it is better to perform feature selection before imputation or vice versa by using the following metrics - the recall, precision, f1-score and accuracy. Due to the fact that there are few literature on this and some debate on the subject among researchers, we hope that the results from this experiment will encourage data scientists and researchers to perform imputation first before feature selection when dealing with data containing missing values.