On the Performance of Imputation Techniques for Missing Values on
  Healthcare Datasets
Luke Oluwaseye Joel Wesley Doorsamy Babu Sena Paul
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.