Table 1.

Data imputation methods.

Methods	Definition	Accuracy	References
Mean value imputation (MVI)	The values are filled using calculating the mean for a missing value	Biased	[42]
Maximum Likelihood (ML)	A likelihood function is evaluated and then sum or integrate over the missing data	Unbiased parameter estimation	[43]
Hot Deck Imputation	A data matrix for all instances created is chosen as a source for missing values	Replication of values may cause bias	[44]
Multiple Imputation (MI)	Starts by introducing random variation and generates several datasets with slightly different imputed values. Statistical analysis on each to find the optimal one	Comparable to ML	[45]
Multivariate Imputation by Chained Equations (MICE)	The method first identifies an imputation model for each column followed by random draws from the observable data	Comparable to ML	[46]
Expectation–Maximization with Bootstrapping (EMB)	Initially the likelihood function is evaluated using model parameters. Next, with the updated parameters, the likelihood function is maximized, and the parameters are updated to return a new distribution	Comparable to ML	[47]