Support Vector Regression 기반 개인화 혈당 예측 알고리즘
- Title
- Support Vector Regression 기반 개인화 혈당 예측 알고리즘
- Authors
- Wonju Seo; Seunghyun Lee; Namho Kim; Jiwon Kim; Sung-Min Park
- Date Issued
- 2019-05-11
- Publisher
- 대한의용생체공학회
- Abstract
- Personalized glucose prediction algorithm (PGPA) is considered as an excellent approach to manage glucose levels due to abilities to consider a patient‘s non-linear glucose patterns. To extract continuous glucose monitoring (CGM) time-series data, 30 virtual patients with type 1 diabetes were generated by UVA/Padova T1DMS. The developed support vector regression that was trained with CGM points collected for 3 days showed 17.7 mg/dL of root mean square errors and 11.6 % of mean absolute percentage error on average. In conclusion, we validated the approach of PGPA with the patients and it will be greatly helpful to manage blood glucose level.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/98866
- Article Type
- Conference
- Citation
- 2019년 대한의용생체공학회 춘계학술대회, 2019-05-11
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.