@article {Benaida2022476,
	title = {Machine and~Deep Learning Predictive Techniques for~Blood Glucose Level},
	journal = {Lecture Notes in Networks and Systems},
	volume = {468 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {476-485},
	abstract = {Allowing diabetic patients to predict their BGL is an important task for self-management of their metabolic disease. This allows to avoid hypo or hyperglycaemia by taking appropriate actions. Currently, this is possible due to the development of machine and deep learning techniques which are successfully used in many prediction tasks. This paper evaluates and compares the performances of six ML/DL techniques to forecast BGL predictions; four DL techniques: CNN, LSTM, GRU, DBN and two ML/statistic techniques: SVR, and AR. The evaluation of the performance of the six regressors were in term of four criteria: RMSE, MAE, MMRE, and PRED. In addition, the Scott-Knott were used to evaluate the statistical significance test and to rank the regressors. The results show that AR was the best for 5~min ahead forecasting with a mean of RMSE equal to 8.67~mg/dl. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04826-5_48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130238876\&doi=10.1007\%2f978-3-031-04826-5_48\&partnerID=40\&md5=8889c0cf2c91459b8f32389a846059f5},
	author = {Benaida, M. and Abnane, I. and Idri, A. and El Idrissi, T.}
}
