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For the classification task, we did not remove missing values but rather replace them by the average value of the dimension.
The obtained results, considering different performance measures, not only suggest this approach improves the quality of a database with missing values, but also the best results are clearly obtained using the Multilayer Perceptron model in data sets with categorical variables.
For CAD diagnostic model cluster-based missing value imputation does not improve the performance of the C4.5 classifier because the features used to construct the CAD model do not contain missing values, but in case of NB Tree and MLP there is significant improvement in accuracy.
We used multiple imputation to replace those missing values, but this requires the strong assumption that the data are missing at random, given the available covariates.
For missing data we did not replace missing values but performed each analysis with available cases.
Note: Some missing values but less than 13% for all variables (Additional file 1: Table S4).
Similar(39)
The bias was therefore rarely associated with a single factor (e.g., a large proportion of missing values alone) but rather with combinations of (adverse) conditions (e.g., a large proportion of missing values and a high level of dependence of the pattern of missing data on true person proficiencies).
Outcome variables did not have missing values imputed, but were included in the imputation models to predict missing values in other variables.
After replacement of missing values all but 2133 cases (4.1%) had complete data on all variables included in the analyses, resulting in a net sample size of 49,425.
Eleven respondents with missing values on some but not all of the choice sets were included.
Again, two missing values were accepted, but not the last two before waking up.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com