Indeed, the score cannot be computed for individuals with one or more missing value, unless using missing value imputation.
Because all respondents answered on questionnaire items we used in this study, we were able to conduct our analyses based on the entire sample participated in the first wave of SWPS without listwise deletion or missing value imputation for handling missing data.
To find an optimal method to solve the missing value problem efficiently, we conducted a comprehensive performance comparison of various missing value imputation algorithms in this work.
For statistical analysis, data were analyzed by using the SPSS software (IBM) through missing value imputation via K-nearest neighbours analysis, followed by log-transformation of the imputed data and comparison of control and treated values to evaluate corresponding variance (ANOVA), with a non-linear mixed-effects model.
All descriptive results and statistical model output were statistics after the missing value imputation.
One may use missing value imputation to increase accuracy of predictive models.
K-means is an unsupervised learning algorithm that can be used for missing value imputation [32].
To use knowledge base sufficiently for missing value imputation, we present FROG, an inference algorithm from knowledge bases.
Various missing value imputation techniques have been employed by researchers, such as case-wise deletion, mean value imputation, maximum likelihood, machine learning algorithms including decision tree and MLP.
The data set is analyzed using a hybrid and novel k-means cluster centroid-based method for missing value imputation and C4.5, NB Tree and multilayer perceptron for modeling to predict CAD patients.
