# Validating and testing prediction model when you have too little cases

I am building a logistic regression (gym function in r) to predict whether a patient is diseases or not. The sample I have now contains 111 diseased and 682 non-diseased. From here, I understand that it is not a good idea to seperate the 793 patients I have into training and validation sets if I have such small number of cases (diseased). My model is likely to have at least five continuous variables and three categorical variables (2 levels, 4 levels and 5 levels respectively). I would probably need to need collapse some levels arising from small counts. How should I go about validating and testing the model given the circumstances I have?