10.1371/journal.pone.0139210.t005 Katya L. Masconi Katya L. Masconi Tandi Edith Matsha-Erasmus Tandi Edith Matsha-Erasmus Rajiv T. Erasmus Rajiv T. Erasmus Andre P. Kengne Andre P. Kengne Overview of the performance of the undiagnosed diabetes risk prediction models across the five multiple imputation datasets. Public Library of Science 2015 Rotterdam Predictive model undiagnosed diabetes South Africa BackgroundImputation techniques undiagnosed diabetes risk prediction models Kuwaiti Risk model Data Imputation Techniques Cambridge Diabetes Risk model imputation methods Omani Diabetes Risk model intercept adjustment.ResultsThe study sample data 2015-09-25 05:17:04 Dataset https://plos.figshare.com/articles/dataset/_Overview_of_the_performance_of_the_undiagnosed_diabetes_risk_prediction_models_across_the_five_multiple_imputation_datasets_/1557822 <p>Overview of the performance of the undiagnosed diabetes risk prediction models across the five multiple imputation datasets.</p>