Predicting blood glucose using an LSTM Neural Network
Touria El Idrissi, Ali Idri, Ibtissam Abnane, Zohra Bakkoury
Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 35–41 (2019)
Abstract. Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network model for blood glucose prediction. The model is a sequential one using a Long- Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic patients to decide on the model's parameters in order to identify the best variant of the model. The performance of the proposed model measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM model and an autoregressive (AR) model. The results show that our model is significantly more accurate; in fact, our LSTM model outperforms the existing LSTM model for all patients and outperforms the AR model in 9 over 10 patients, besides, the performance differences were assessed by thWilcoxon statistical test. Furthermore, the mean of the RMSE of our model was 12.38 mg/dl while it was 28.84 mg/dl and 50.69 mg/dl for AR and the existing LSTM respectively.
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