One hundred samples is not a lot to work with in terms of statistics, but it may end up being sufficient if your variables are informative.

One can assess whether features are informative to the final outcome or not by plotting histogram distributions of your features for both final outcomes. Let's say that our features are called 33 and 34 and we plot their histogram distributions for conditions 0 (no diabetes) and 1 (yes diabetes).

When the two distributions overlap completely or to a high degree, that feature will not be very useful. When the distributions are different, that feature is better at discriminating the two final outcomes. In this case feature 33 is more informative than 34. By studying the image one can quickly come up with a simple rule that when 33 is larger than 0, the final outcome is more likely to be 1; when values of 33 are smaller than 0, it is more likely that the outcome will be 0. No such strong rule is obvious for 34, though in that case as well one can make out two weak rules: 1) it is slightly more likely that the outcome is 1 when feature 34 has extreme positive or negative values; 2) it is slightly more likely that the outcome is 0 when feature 34 is just below 0.

All of this is for visualization purposes only - I am not suggesting that you come up with classification rules by eyeballing histograms. Any machine learning method that knows how to deal with feature importance (tree-based methods, L1-regularized regression) will extract feature importance automatically, though maybe not very accurately for only 100 samples.