Comparison of the Most Influential Missing Data Imputation Algorithms for Healthcare

In healthcare research, the reliability of input data is essential. However, missing data is a common incident in this field for various reasons. Current research mainly focuses on developing new data imputation methodologies, while there is a need for studying on a global evaluation of existing algorithms. In this research, we compared the performance of four influential missing data imputation algorithms, Regularized Expectation-Maximization (EM), Multiple Imputation (MI), kNN Imputation (kNNI) and Mean Imputation on two real health care datasets: (1) MHEALTH dataset and (2) the University of Queensland Vital Signs dataset. Under the Missing Completely At Random (MCAR) assumption, Root Mean Squared Error (RMSE) and execution time were used as best performing evaluation criteria. The experimental analysis suggests that EM is the imputation algorithm which is expected to be a good choice to deal with the problem of missing data in the healthcare area.