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The proposed imputation technique employs Probabilistic Neural Network (PNN) preceded by mode for imputing the missing categorical data.
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k nearest neighbor imputation (kNNI) is one of the most popular methods in empirical software engineering for imputing missing values.
Several programs are available for imputing genotype data, including IMPUTE 2 (8), MaCH (9), and Beagle (10).
Last observation carried forward was used for imputing missing data.
The treatment group-specific median or mode was imputed for missing continuous and categorical variables, respectively.
All variables had < 10% missing data; therefore, missing values were imputed with the median or mode for continuous or categorical variables, respectively.
We used the mode for categorical variables and mean for continuous variables to impute missing values.
Missing data for these variables were imputed using unconditional imputation: imputation of the mean for continuous variables or the mode for categorical variables [20].
The mean (for continuous variables) or mode (for categorical variables) of the non-missing values of each variable were used to impute the missing values.
Missing data were imputed with the sample median for continuous or ordinal variables, and the mode for dichotomous variables.
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