Identifying Clinical Predictors of Diabetes and Prediabetes: An Explainable AI Approach Using Primary Care Electronic Medical Records

Prediabetes (PD), a reversible metabolic condition that precedes type 2 diabetes (T2DM), carries a high risk of progression to T2DM, but can be effectively treated by restoring normal blood glucose levels, mainly through lifestyle changes such as diet and physical activity. This study uses data from 21'023 patients from Canadian primary care Electronic Medical Records (EMRs), expanding a pre-existing clinical dataset by adding detailed information on drug prescriptions and smoking status, to train and compare different machine learning models to predict future normoglycemia, PD, or T2DM. The use of explainability techniques enhances our understanding of the progression from PD to T2DM, emphasizing the crucial role of blood sugar levels, body mass index, cholesterol levels and blood pressure in determining T2DM risk. These results highlight the potential of routinely collected clinical data to support early identification of high-risk individuals, enabling preventive interventions. Future studies should validate these findings in prospective cohorts and expand the set of investigated features to further improve the model's predictive performance and generalizability.