Development and validation of a type 2 diabetes machine learning classification model for EHR-based diagnostics and clinical decision support

Abstract

Undiagnosed type 2 diabetes is very common and represents a significant challenge for all national healthcare systems. Although diagnostic criteria and laboratory screening procedures are well-established, clinical tests have limitations, and in many cases diagnosis confirmation and more precise interpretation of the tests results are required. Machine learning methods, when applied to clinical outcomes risk prediction, demonstrate great effectiveness as they recognize specific patterns in data dynamics and thus can be used for identification of at-risk cases where diabetes and complications can be delayed or even prevented. We developed and validated a machine learning classification model for type 2 diabetes that possesses several important advantages over conventional methods (FINDRISC, ADA risk score), including mean values of 0.959, 0.92 and 0.89 for AUC, specificity and sensitivity, respectively. The study results potentially have major clinical implication, as the model is able to exchange data with electronic health records. Thus, it can be employed in clinical decision support framework together with other diabetes, cardiovascular disease models and models for related conditions