Proceedings of the 2019 11th International Conference on Machine Learning and Computing 2019
DOI: 10.1145/3318299.3318354
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Blood Glucose Level Prediction with Minimal Inputs Using Feedforward Neural Network for Diabetic Type 1 Patients

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Cited by 7 publications
(5 citation statements)
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“…This paper [4], they have investigated virtual CGM data of 10 subjects taken from AIDA directory to predict the future diabetic concentration using Feed Forward Neural Networks (FNN). Here, the previous blood glucose values are only taken under consideration for the prediction of future values.…”
Section: Fig1: Block Diagram Of Taiyu Zhu 2018 22 Muhammad Asad 2019mentioning
confidence: 99%
“…This paper [4], they have investigated virtual CGM data of 10 subjects taken from AIDA directory to predict the future diabetic concentration using Feed Forward Neural Networks (FNN). Here, the previous blood glucose values are only taken under consideration for the prediction of future values.…”
Section: Fig1: Block Diagram Of Taiyu Zhu 2018 22 Muhammad Asad 2019mentioning
confidence: 99%
“…Many studies on the prediction of BGLs have been conducted, each using a different set of data, patients, and features [ 15 ]. For example, a study used recurrent artificial neural networks (ANNs) and Elman recurrent ANNs to predict BGLs based on previous blood glucose values.…”
Section: Introductionmentioning
confidence: 99%
“…Blood glucose homeostasis is an important measure of the health status of diabetic patients, and, as there is no cure for diabetes, it is important for the control and treatment of diabetic patients if the blood glucose level can be predicted quickly and accurately. Based on the data provided by CGMS, researchers have proposed various prediction methods to predict blood glucose values by building data-driven models, such as autoregressive (AR) [ 5 , 6 , 7 ] models, support vector machines [ 8 , 9 ], and neural networks [ 10 , 11 ]. In 2018, Takoua.…”
Section: Introductionmentioning
confidence: 99%