Blood glucose level prediction based on support vector regression using mobile platforms

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autor(en): Reymann M, Dorschky E, Groh B, Martindale C, Blank P, Eskofier B
Jahr der Veröffentlichung: 2016
Tagungsband: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Sprache: Englisch


Abstract


The correct treatment of diabetes is vital to a patient’s health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body’s response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.



FAU-Autoren / FAU-Herausgeber

Blank, Peter
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Dorschky, Eva
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Eskofier, Björn Prof. Dr.
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Groh, Benjamin
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Martindale, Christine
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)


Zitierweisen

APA:
Reymann, M., Dorschky, E., Groh, B., Martindale, C., Blank, P., & Eskofier, B. (2016). Blood glucose level prediction based on support vector regression using mobile platforms. In 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, Florida, USA, US.

MLA:
Reymann, Maximilian, et al. "Blood glucose level prediction based on support vector regression using mobile platforms." Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA 2016.

BibTeX: 

Zuletzt aktualisiert 2018-19-04 um 03:38