Transfer learning in hypoglycemia classification
Publication date
2024-08-14
Document type
Conference paper
Author
Organisational unit
Conference
1. International Conference on AI in Healthcare (AIiH 2024) ; Swansea, UK ; September 4–6, 2024
Series or journal
Lecture Notes in Computer Science
Periodical volume
14975
Book title
Artificial intelligence in healthcare
Volume (part of multivolume book)
1
First page
98
Last page
109
Peer-reviewed
✅
Part of the university bibliography
✅
Keyword
Transfer learning
Hypoglycemia classification
T1D
T2 pre-diabetes
Abstract
Patients with type 1 diabetes (T1D) have a higher risk of experiencing hypoglycemia, which is a severe condition of decreased blood glucose levels under 70 mg/dL and can result in coma, or death in the worst case. Prediction algorithms could improve diabetes care by enabling preventive actions, but research is limited by available multivariate datasets. Thus, this work investigates the feasibility of transfer learning between two different datasets of people with T1D and type 2 pre-diabetes using a 1 Dimensional Convolutional Neural Network (1DCNN) model. Moreover, different thresholds for defining hypoglycemia are compared for the pre-diabetes group. The results show that transfer learning could be feasible if the model is trained on T1D with a threshold of 70 mg/dL, while a threshold of 80–85 mg/dL achieved the best training performance for the pre-diabetes data.
Cite as
Cinar, B., Grensing, F., van den Boom, L., Maleshkova, M. (2024). Transfer Learning in Hypoglycemia Classification. In: Xie, X., Styles, I., Powathil, G., Ceccarelli, M. (eds) Artificial Intelligence in Healthcare. AIiH 2024. Lecture Notes in Computer Science, vol 14975. Springer, Cham. https://doi.org/10.1007/978-3-031-67278-1_8
Version
Published version
Access right on openHSU
Metadata only access