Identifying Continuous Glucose Monitoring Data Using Machine Learning.

Background and Aims: The recent increase in wearable devices for diabetes care, and in particular the use of continuous glucose monitoring (CGM), generates large data sets and associated cybersecurity challenges. In this study, we demonstrate that it is possible to identify CGM data at an individual level by using standard machine learning techniques. Methods: The publicly available REPLACE-BG data set (NCT02258373) containing 226 adult participants with type 1 diabetes (T1D) wearing CGM over 6 months was used. A support vector machine (SVM) binary classifier aiming to determine if a CGM data stream belongs to an individual participant was trained and tested for each of the subjects in the data set. To generate the feature vector used for classification, 12 standard glycemic metrics were selected and evaluated at different time periods of the day (24 h, day, night, breakfast, lunch, and dinner). Different window lengths of CGM data (3, 7, 15, and 30 days) were chosen to evaluate their impact on the classification performance. A recursive feature selection method was employed to select the minimum subset of features that did not significantly degrade performance. Results: A total of 40 features were generated as a result of evaluating the glycemic metrics over the selected time periods (24 h, day, night, breakfast, lunch, and dinner). A window length of 15 days was found to perform the best in terms of accuracy (86.8% ± 12.8%) and F1 score (0.86 ± 0.16). The corresponding sensitivity and specificity were 85.7% ± 19.5% and 87.9% ± 17.5%, respectively. Through recursive feature selection, a subset of 9 features was shown to perform similarly to the 40 features. Conclusion: It is possible to determine with a relatively high accuracy if a CGM data stream belongs to an individual. The proposed approach can be used as a digital CGM "fingerprint" or for detecting glycemic changes within an individual, for example during intercurrent illness.