Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

Abstract

Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>RMSE</mml:mtext></mml:mrow><mml:mrow><mml:mn>5</mml:mn><mml:mtext> </mml:mtext><mml:mtext>day</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>) not used during ANN training. For BGL predictions of up to 1 hour a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>RMSE</mml:mtext></mml:mrow><mml:mrow><mml:mn>5</mml:mn><mml:mtext> </mml:mtext><mml:mtext>day</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>of (±SD)<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0.15</mml:mn><mml:mo>±</mml:mo><mml:mn>0.04</mml:mn></mml:math> mmol/L was observed. For BGL predictions up to 10 hours, a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>RMSE</mml:mtext></mml:mrow><mml:mrow><mml:mn>5</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mtext>day</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>of (±SD)<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0.14</mml:mn><mml:mo>±</mml:mo><mml:mn>0.16</mml:mn></mml:math> mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.