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A task of vital clinical importance, within Diabetes management, is theprevention of hypo/hyperglycemic events. Increasingly adopted ContinuousGlucose Monitoring (CGM) devices offer detailed, non-intrusive and real timeinsights into a patient's blood glucose concentrations. Leveraging advancedMachine Learning (ML) Models as methods of prediction of future glucose levels,gives rise to substantial quality of life improvements, as well as providing avital tool for monitoring diabetes.  A regression based prediction approach is implemented recursively, with aseries of Machine Learning Models: Linear Regression, Hidden Markov Model,Long-Short Term Memory Network. By exploiting a patient's past 11 hours ofblood glucose (BG) concentration measurements, a prediction of the 60 minutesis made. Results will be assessed using performance metrics including: RootMean Squared Error (RMSE), normalised energy of the second-order differences(ESOD) and F1 score.  Research of past and current approaches, as well as available dataset, led tothe establishment of an optimal training methodology for the CITY dataset,which may be leveraged by future model development. Performance was alignedwith similar state-of-art ML models, with LSTM having RMSE of 28.55, however nosignificant advantage was observed over classical Auto-regressive AR models.Compelling insights into LSTM prediction behaviour could increase public andlegislative trust and understanding, progressing the certification of ML modelsin Artificial Pancreas Systems (APS).

Machine Learning based prediction of Glucose Levels in Type 1 Diabetes Patients with the use of Continuous Glucose Monitoring Data