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Zueger, Thomas ORCID logoORCID: https://orcid.org/0000-0001-6190-7405; Schallmoser, Simon; Kraus, Mathias; Saar-Tsechansky, Maytal; Feuerriegel, Stefan ORCID logoORCID: https://orcid.org/0000-0001-7856-8729 and Stettler, Christoph (2022): Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes. In: Diabetes Technology & Therapeutics, Vol. 24, No. 11: pp. 842-847

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Traditional risk scores for the prediction of type 2 diabetes (T2D) are typically designed for a general population and, thus, may underperform for people with prediabetes. In this study, we developed machine learning (ML) models predicting the risk of T2D that are specifically tailored to people with prediabetes. We analyzed data of 13,943 individuals with prediabetes, and built a ML model to predict the risk of transition from prediabetes to T2D, integrating information about demographics, biomarkers, medications, and comorbidities defined by disease codes. Additionally, we developed a simplified ML model with only eight predictors, which can be easily integrated into clinical practice. For a forecast horizon of 5 years, the area under the receiver operating characteristic curve was 0.753 for our full ML model (79 predictors) and 0.752 for the simplified model. Our ML models allow for an early identification of people with prediabetes who are at risk of developing T2D.

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