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.
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References
WHO Organization: WHO Diabetes (2020). https://www.who.int/news-room/fact-sheets/detail/diabetes
Bilous, R., Donnelly, R.: Handbook of Diabetes. Wiley (2010)
Orchard, T., Secrest, A., Miller, R., Costacou, T.: In the absence of renal disease, 20 year mortality risk in type 1 diabetes is comparable to that of the general population: a report from the Pittsburgh epidemiology of diabetes complications study. Diabetologia 53, 2312–2319 (2010)
El Idrissi, T., Idri, A., Kadi, I., Bakkoury, Z.: Strategies of multi-step-ahead forecasting for blood glucose level using LSTM neural networks: a comparative study. In: HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems And Technologies, BIOSTEC 2020, pp. 337–344 (2020)
El Idrissi, T., Idri, A., Bakkoury, Z.: Data mining techniques in diabetes self-management: a systematic map. In: World Conference on Information Systems And Technologies, pp. 1142–1152 (2018)
Jayatilake, S., Ganegoda, G.: Involvement of machine learning tools in healthcare decision making. J. Healthcare Eng. 2021, 6679512 (2021)
Idrissi, T., Idri, A., Bakkoury, Z.: Systematic map and review of predictive techniques in diabetes self-management. Int. J. Inf. Manage. 46, 263–277 (2019)
Woldaregay, A., et al.: Data-driven modeling and prediction of blood glucose dynamics: machine learning applications in type 1 diabetes. Artif. Intell. Med. 98, 109–134 (2019)
Wadghiri, M., Idri, A., Idrissi, T.: Ensemble regression for blood glucose prediction. In: World Conference On Information Systems and Technologies, pp. 544–554 (2021)
McQuarrie, A., Tsai, C.: Regression and Time Series Model Selection. World Scientific (1998)
Smola, A.: Others Regression estimation with support vector learning machines. Master’s thesis, Technische Universität München (1996)
Lu, C., Lee, T., Chiu, C.: Financial time series forecasting using independent component analysis and support vector regression. Decis. Support Syst. 47, 115–125 (2009)
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: Handbook Brain Theory and Neural Networks, vol. 3361, p. 1995 (1995)
Gu, J., et al.: Recent advances in convolutional neural networks (2015). eprint. ArXiv Preprint. ArXiv:1512.07108
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Ren, Y., Mao, J., Liu, Y., Li, Y.: A novel DBN model for time series forecasting. IAENG Int. J. Comput. Sci. 44, 79–86 (2017)
Hua, Y., Guo J., Zhao, H.: Deep belief networks and deep learning. In: Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, pp. 1–4 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Sun, Q., Jankovic, M., Bally, L., Mougiakakou, S.: Predicting blood glucose with an LSTM and Bi-LSTM based deep neural network. In: 2018 14th Symposium on Neural Networks and Applications, NEUREL, pp. 1–5 (2018)
El Idriss, T., Idri, A., Abnane, I., Bakkoury, Z.: Predicting blood glucose using an LSTM neural network. In: 2019 Federated Conference on Computer Science and Information Systems, FedCSIS, pp. 35–41 (2019)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). ArXiv Preprint ArXiv:1406.1078
Zhang, S., Abdel-Aty, M., Wu, Y., Zheng, O.: Modeling pedestrians’ near-accident events at signalized intersections using gated recurrent unit (GRU). Accid. Anal. Prev. 148, 105844 (2020)
DirecNet Diabetes Research in Children Network (DirecNet). http://direcnet.jaeb.org/Studies.aspx. Accessed 27 June 2020
Idri, A., Abnane, I., Abran, A.: Evaluating PRED (p) and standardized accuracy criteria in software development effort estimation. J. Soft. Evol. Process 30, e1925 (2018)
Zerouaoui, H., Idri, A., Nakach, F., Hadri, R.: breast fine needle cytological classification using deep hybrid architectures. In: International Conference on Computational Science and its Applications, pp. 186–202 (2021)
Scott, A., Knott, M.: A cluster analysis method for grouping means in the analysis of variance. Biometrics 30, 507–512 (1974)
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Benaida, M., Abnane, I., Idri, A., El Idrissi, T. (2022). Machine and Deep Learning Predictive Techniques for Blood Glucose Level. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-04826-5_48
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