Data Mining Techniques in Diabetes Self-management: A Systematic Map

Idrissi, Touria El; Idri, Ali; Bakkoury, Zohra

doi:10.1007/978-3-319-77712-2_109

Cited by 6 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ATA mining (DM) techniques are useful tools for extracting valuable knowledge from (large) databases that helps in decision making [1], [2]. DM has been fruitfully used in different subfields of medical informatics such as diabetes [1], [3], cardiology [1], [4] and cancer [1], [5].…”

Section: Introductionmentioning

confidence: 99%

Predicting blood glucose using an LSTM Neural Network

Idriss¹,

Idri

Abnane

et al. 2019

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

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 (NN) model for blood glucose prediction. It 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 it. The performance of the proposed LSTM NN measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM and an autoregressive (AR) models. The results show that our LSTM NN is significantly more accurate; in fact, it 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 the Wilcoxon 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.

show abstract