The researchers of the Hellenic Research & Technology Center (EKETA) – Institute of Information Technologies (ITY), discovered that it is possible to predict hypoglycemic episodes based on the use of common consumer devices for measuring glucose and blood pressure, in combination with the forDiabetes mobile application.
See the new study published in IEEE Access https://ieeexplore.ieee.org/document/10381718
The study
“Next-day prediction of hypoglycaemic episodes based on the use of a mobile app for diabetes self-management”
Publisher: IEEE (published in IEEE Access)
Date of publication: 05 January 2024
Electronic ISSN: 2169-3536
DOI: 10.1109/ACCESS.2024.3350201
Abstract
Hypoglycaemia is one of the most common complications in diabetes, which can be life threatening if not managed appropriately. So far, research on hypoglycaemia prediction has been scarce, focusing on small cohorts linked to specific geographical regions, thus limiting the generalizability of the findings.
In this paper, we developed and validated different machine learning models for next-day hypoglycaemia prediction in type 2 diabetes. We used a large international cohort comprising 669 participants, who had been regular users (for over a couple of years) of a mobile app for diabetes self-management and used common portable commercial devices for measuring their blood glucose and blood pressure levels, collecting in total 96121 observations (from which we extracted a balanced dataset of 2998 observations).
Random Forests (RF), Support Vector Machines, Adaptive Boosting and Feed-Forward Artificial Neural Networks were employed to train predictive models based on 10-day temporal sequences with blood glucose and blood pressure measurements towards estimating next day hypoglycaemic episodes. We used a leave-one-subject-out (LOSO) approach for model validation, and found that RF achieved the best accuracy (0.814) and F1-score (0.812) with sensitivity (0.805) and specificity (0.824) for next-day hypoglycaemia prediction.
The results of this study provide an expedient and reliable app-based approach to accurately predict hypoglycaemia in day-to-day life, thereby facilitating patient and care provider awareness and potentially preventing other serious complications.
Download the PDF file of the entire study.
