Type Two Diabetes in Latvia: Prevalence, Treatment, and the Potential for AI-Based Mitigation

Background and Objectives: Diabetes mellitus (Type 2 diabetes) is a significant and rapidly growing global healthcare challenge and a leading cause of severe complications, including cardiovascular disease (CVD), chronic kidney disease and other conditions. In Latvia more than 103 000 individuals have been registered in the Latvian Diabetes Patient Register, of whom 90% had type 2 diabetes. Since early diagnosis can reduce complication risk, considerable effort has been devoted to the development of numerous CVD risk prediction tools using traditional statistics as well as data science-based methods. This article augments these efforts with predictive models built specifically for Latvia’s diabetes patient population. Materials and Methods: Models used anonymized diabetes patient data stored at Latvia’s Center for Disease Prevention and Control containing passively collected information on patients’ utilization of publicly funded healthcare services. No clinical data was included. In accordance with best practices in model development, the 96 302 patient records were randomly partitioned into a 70% training data portion and 30% for out-of-sample validation. We tested three modeling methodologies—logistic regression, Random Forests, and XGBoost. Results: The binary dependent variable was defined as the occurrence of a CVD-related hospitalization during 2023 or 2024. The predictor variables were limited to data from the prior two year period, 2021-2022. The three model types all produced similar fit to the data—area under the Receiver Operating Curve (ROC) of 0,70-0,71 in the validation data, which is comparable to or better than results obtained in other studies, including SCORE2. Conclusions: We find CVD risk prediction models based on passively collected Latvian electronic health records to be feasible. However, they are primarily useful for predicting, not explaining outcomes. Our model identifies a group of 8 000—10 000 diabetes patients with an elevated (40%-45%) risk of experiencing a CVD-related hospitalization over the next two years. We recommend they be targeted for monitoring and proactive, preventative care.