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Piia Lavikainen,1,* Gunjan Chandra,2,* Pekka Siirtola,2 Satu Tamminen,2 Anusha T Ihalapathirana,2 Juha Röning,2 Tiina Laatikainen,3– 5 Janne Martikainen1 1School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 2Biomimetics and Intelligent Systems Group, Faculty of ITEE, University of Oulu, Oulu, Finland; 3Joint Municipal Authority for North Karelia Social and Health Services (Siun Sote), Joensuu, Finland; 4Department of Public Health and Social Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; 5Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland*These authors contributed equally to this workCorrespondence: Piia Lavikainen, School of Pharmacy C/O Faculty of Health Sciences, University of Eastern Finland, P.O. Box 1627, Kuopio, FI-70211, Finland, Tel +358 40 7024682, Email piia.lavikainen@uef.fiPurpose: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors.Patients and Methods: Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually.Results: Three HbA1c trajectories were distinguished over six years: “stable, adequate” (86.5%), “improving, but inadequate” (7.3%), and “fluctuating, inadequate” (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most im