  This paper reviews a wide selection of machine learning models built to
predict both the presence of diabetes and the presence of undiagnosed diabetes
using eight years of National Health and Nutrition Examination Survey (NHANES)
data. Models are tuned and compared via their Brier Scores. The most relevant
variables of the best performing models are then compared. A Support Vector
Machine with a linear kernel performed best for predicting diabetes, returning
a Brier score of 0.0654 and an AUROC of 0.9235 on the test set. An elastic net
regression performed best for predicting undiagnosed diabetes with a Brier
score of 0.0294 and an AUROC of 0.9439 on the test set. Similar features appear
prominently in the models for both sets of models. Blood osmolality, family
history, the prevalance of various compounds, and hypertension are key
indicators for all diabetes risk. For undiagnosed diabetes in particular, there
are ethnicity or genetic components which arise as strong correlates as well.
