The University of Chicago National Monogenic Diabetes Registry houses a novel dataset of people presenting with possible or known monogenic diabetes and has over a decade of data points, despite the rarity of the disease relative to T1DM and especially T2DM. Currently, determination of genetic testing, including which specific panel or genes is most appropriate to sequence, is a manual process introducing variability and subjectivity. Aim: To test and compare performance of several models against the manual process and each other in identifying maturity-onset diabetes of the young (MODY) from available clinical information. Methods: Statistical models for prediction of monogenic diabetes were applied to clinical data for cases suspected of having MODY. The models tested in the study with this dataset ranged from the traditional methods of regression to more modern approaches of neural nets, classification trees, clustering, and other machine learning algorithms for classification of MODY against type 1 and type 2 and a possible subset of monogenic diabetes subtypes. Results: Using a sample size of N=330, with 70 testing positive for either GCK, HNF1A, or HNF4A, exploratory data models were able to achieve 75-80% validation accuracy (random validation data set at 10-20% of N) while internal human classification accuracy was roughly 42% over the same samples. The traditional logistic regression and neural net had equivalent performance. Conclusions: The 3750 participants within the Registry and more than a decade of follow-up presents a unique opportunity to assess which clinical features are most predictive in identifying those with monogenic diabetes. The results of this work will direct future testing efforts within the Registry to assist in efficient diagnosis of these uncommon yet clinically important forms of diabetes. Disclosure R. P. Mulligan: None. L. R. Letourneau-freiberg: None. T. L. Bowden: None. P. Tian: None. B. Kandasamy: None. L. H. Philipson: Advisory Panel; Self; Nevro Corp., Research Support; Self; Provention Bio, Inc. S. W. Greeley: None. R. N. Naylor: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK104942, P30DK020595)