Binary encoding for medical features 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?

Don't feel hamstrung by the somewhat arbitrary "normal" limits of these clinical variables.* A linear model of a continuous predictor uses up one degree of freedom, just like discretizing it into two categories does. Discretizing it into "low," "normal" and "high" uses up 2 degrees of freedom; why not use that many degrees of freedom for a simple continuous spline fit instead?
You do need to do some data reduction, but discretizing continuous variables isn't the way to proceed. Frank Harrell discusses this matter extensively with clinical data, in Section 4.7 of his course notes and book. You work with the predictors while ignoring the outcomes, for example removing predictors with low variance and taking advantage of their correlations with each other to reduce multiple predictors to single cluster scores.

*I remember when someone I know was told frantically by a physician that she had a "low sodium" and should eat more salt and drink less water, because her sodium was 134 mEq/liter instead of the "normal" 135. Clinicians don't always appreciate random variability (or maybe aren't allowed to, for medico-legal reasons).
