Abstract
In this paper, we describe a novel approach to the prediction of human blood glucose levels by analysing rich biometric human contextual data from a pioneering lifelog dataset. Numerous prediction models (RF, SVM, XGBoost and Elastic-Net) along with different combinations of input attributes are compared. An efficient ensemble method of stacking of multiple combination of prediction models was also implemented as our contribution. It was found that XGBoost outperformed three other models and that a stacking ensemble method further improved the performance.
Keywords
- Blood glucose
- Lifelogging
- Human context
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Palbar, T., Kesavulu, M., Gurrin, C., Verbruggen, R. (2022). Prediction of Blood Glucose Using Contextual LifeLog Data. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13141. Springer, Cham. https://doi.org/10.1007/978-3-030-98358-1_32
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DOI: https://doi.org/10.1007/978-3-030-98358-1_32
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