Predicting Future Glucose Fluctuations Using Machine Learning and Wearable Sensor Data

Abstract

Predicting blood glucose values (BG) using machine learning (ML) algorithms and data fusion techniques. There has been a recent explosion of interest in BG prediction due to its application in the development of insulin regulating algorithms for the Artificial Pancreas Project. During this study, we measured the predictive accuracy of a software system designed to predict glucose behaviour using step-count and heart-rate data in addition to BG-insulin dynamics. The software was tested in a blinded pilot study at BC Children's Hospital for 9 type 1 diabetic children. Using continuous glucose monitors (CGM) and fitness wearables (Fitbit), the software aggregated 60-days of continuous data from each participant. The data from the first 30-days of the study was used to train the algorithem. The trained algorithm was then used to make predictions every 5mins for the next 30days. On average, the software was able to predict user's future glucose values with 93% accuracy rate for 60-mins ahead of time. Although encouraging, the algorithm required further testing. We have since released the app under the commercial name "DiaBits" for more testing and further data collection. Disclosure A. Hayeri: Other Relationship; Self; Dexcom, Inc., Fitbit, Inc.. Research Support; Self; BC Children's Hospital - Vancouver Canada. Other Relationship; Self; University Of British Columbia.