I am trying to build different predictive models using electronic health records. As they have missing values (between 0.5-18% missing values in each feature) I executed multiple imputation using MICE (the R package mice), taking into account van Buurens et al. recommendations and instructions. As the final step in MI is to pool the results (combining inferences from imputed data sets) and as I want to use different learning algorithms in a cross-fold validation set up, following question arises:
Can I use each imputation (having for example 5) seperately in order to build a model, cross-validate it and afterwards (having 5 different modeling results) average the obtained measures (accuracy, sensitiviy, etc.) and calculate the standard deviation in order to obtain one valid, representative result?
I would be very thankful if someone could help me out here!