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

TL;DR

This study compares seven imputation techniques on healthcare datasets with missing values, evaluating their accuracy and impact on feature selection, and finds Missforest and MICE to be the most effective methods.

Contribution

It provides a comprehensive comparison of multiple imputation methods on healthcare data and investigates the effect of feature selection order on imputation performance.

Findings

Missforest outperforms other imputation techniques in accuracy.

Performing imputation before feature selection yields better results.

Imputation methods significantly affect downstream machine learning performance.

Abstract

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.