I'm currently facing a classification task on a rather small set (201 observations) with a large number of predictors (about 90). There are about 25 variables describing such quantities as blood pressure, creatinine level, glucose level, LDL, HDL, CRP, and so on. A common feature for these variables is that the norms for healthy patients are well defined, so i.e. 70-99mg/dl for glucose is considered proper.
Does is then make sense to discretize such variables? When it comes to glucose, value 0 would correspond to the values in the range from 70 to 99, while outside this range it would be 1.
What's the reasoning? Well, my datset is rather small and even reducing the number of predictors may not provide a sufficient number of observations per variable. The second thing is that in such fashion we feed the model with some external knowledge that is not available for it. Is this a good idea?