  Automated learning of patients demographics can be seen as multi-label
problem where a patient model is based on different race and gender groups. The
resulting model can be further integrated into Privacy-Preserving Data Mining,
where it can be used to assess risk of identification of different patient
groups. Our project considers relations between diabetes and demographics of
patients as a multi-labelled problem. Most research in this area has been done
as binary classification, where the target class is finding if a person has
diabetes or not. But very few, and maybe no work has been done in multi-labeled
analysis of the demographics of patients who are likely to be diagnosed with
diabetes. To identify such groups, we applied ensembles of several multi-label
learning algorithms.
