  We augment linear Support Vector Machine (SVM) classifiers by adding three
important features: (i) we introduce a regularization constraint to induce a
sparse classifier; (ii) we devise a method that partitions the positive class
into clusters and selects a sparse SVM classifier for each cluster; and (iii)
we develop a method to optimize the values of controllable variables in order
to reduce the number of data points which are predicted to have an undesirable
outcome, which, in our setting, coincides with being in the positive class. The
latter feature leads to personalized prescriptions/recommendations. We apply
our methods to the problem of predicting and preventing hospital readmissions
within 30-days from discharge for patients that underwent a general surgical
procedure. To that end, we leverage a large dataset containing over 2.28
million patients who had surgeries in the period 2011--2014 in the U.S. The
dataset has been collected as part of the American College of Surgeons National
Surgical Quality Improvement Program (NSQIP).
