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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 AUROCarXiv:2105.09379v1 fatcat:3ugmxnepivdvvph4hbldu2ipc4