  A task of vital clinical importance, within Diabetes management, is the
prevention of hypo/hyperglycemic events. Increasingly adopted Continuous
Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time
insights into a patient's blood glucose concentrations. Leveraging advanced
Machine Learning (ML) Models as methods of prediction of future glucose levels,
gives rise to substantial quality of life improvements, as well as providing a
vital tool for monitoring diabetes.
  A regression based prediction approach is implemented recursively, with a
series of Machine Learning Models: Linear Regression, Hidden Markov Model,
Long-Short Term Memory Network. By exploiting a patient's past 11 hours of
blood glucose (BG) concentration measurements, a prediction of the 60 minutes
is made. Results will be assessed using performance metrics including: Root
Mean 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 to
the establishment of an optimal training methodology for the CITY dataset,
which may be leveraged by future model development. Performance was aligned
with similar state-of-art ML models, with LSTM having RMSE of 28.55, however no
significant advantage was observed over classical Auto-regressive AR models.
Compelling insights into LSTM prediction behaviour could increase public and
legislative trust and understanding, progressing the certification of ML models
in Artificial Pancreas Systems (APS).
