Authors:
Mohamed Zaim Wadghiri
1
and
Ali Idri
1
;
2
Affiliations:
1
Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
;
2
Mohammed VI Polytechnic University, Benguerir, Morocco
Keyword(s):
Ensemble Learning, Machine Learning, Bagging, Blood Glucose, Prediction.
Abstract:
Blood Glucose (BG) prediction is an essential process for diabetes self-management. Many papers investigated the use of various machine learning techniques to design and implement BGL predictors. However, due to the complexity of glucose dynamics, single techniques do not always capture inter- and intra-patient changes. On the other hand, ensemble learning and bagging ensembles in particular have been established to show better performance in many medical disciplines including diabetology. The aim of the present paper is to build BG predictors based on bagging in order to compare their performance to the accuracy of their underlying single techniques and to verify if a particular ensemble outperforms the others. An approach has been proposed to build bagged predictors based on five techniques: LSTM, GRU, CNN, SVR and DT. The models’ performance has been evaluated and compared at a prediction horizon of 30 minutes according to RMSE and CEGA. The results show that the performance of th
e constructed bagging ensembles is very comparable to their underlying single techniques except for regression trees. This can be attributed to the good accuracy of deep learning models but also to the non-stationarity of BG time series that need to be addressed before constructing the bootstrap samples.
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