On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets
<|reference_start|>On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets: 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.<|reference_end|>
@article{joel2024on,
    title={On the Performance of Imputation Techniques for Missing Values on
  Healthcare Datasets},
    author={Luke Oluwaseye Joel and Wesley Doorsamy and Babu Sena Paul},
    journal={arXiv preprint arXiv:2403.14687},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14687},
    primaryClass={cs.LG cs.AI}
}