I am trying to develop a predictive model using high-dimensional clinical data including laboratory values. The data space is sparse with 5k samples and 200 variables. The idea is to rank the variables using a feature selection method (IG, RF etc) and use top-ranking features for developing a predictive model.
While feature selection is going well with a Naïve Bayes approach, I am now hitting an issue in implementing a predictive model due to missing data (NA) in my variable space. Is there any machine learning algorithm that can carefully handle samples with missing data?