Artificial neural network for blood glucose level prediction

Hamdi, Takoua; Ali, Jaouher Ben; Fnaiech, Nader; Costanzo, V. Di; Fnaiech, Farhat; Moreau, Éric; Ginoux, Jean-Marc

doi:10.1109/sm2c.2017.8071825

Cited by 23 publications

(9 citation statements)

References 15 publications

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“…Similarly, for subject 2, for the optimal feedforward neural network, RMSE is 1.43 and 3.51 ml/dl which is improved using the optimal autoregressive neural network to 0.7911and 1.6756 ml/dl for 15 min and 30 min prediction horizons, respectively. We further validated our proposed model using UCI machine learning datasets (Abalone and Servo), and it has shown improved results as well [1,6,7]. e organization of the paper is as follows: Section 2 contains related work, Section 3 describes the system architecture, Section 4 presents the model description and BGL performance prediction, and Section 5 shows results and analysis, whereas in Section 6, there is conclusion presenting the summary of the work done in this research and future work suggested.…”

Section: Introductionmentioning

confidence: 93%

“…In [11], virtual data from the free online diabetic simulator, AIDA, were used to predict future glucose level using artificial neural intelligence. In the study [1], the artificial neural network (ANN) is used for accurate blood glucose level prediction of type 1 diabetes (T1D). For validation, 12 real patients' data were used.…”

Section: Related Workmentioning

confidence: 99%

“…e reason for this deficiency is the destruction of pancreatic β-cells which can further result in affecting the capability of the pancreas to produce sufficient insulin. T1D can be very harmful to the health of a person, and nowadays, it is progressively increasing at the rate of 3% per year [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Asad

Qamar

Abbas

2021

Journal of Healthcare Engineering

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Diabetes type 1 is a chronic disease which is increasing at an alarming rate throughout the world. Studies reveal that the complications associated with diabetes can be reduced by proper management of the disease by continuously monitoring and forecasting the blood glucose level of patients. Objective. The prior prediction of blood glucose level is necessary to overcome the lag time for insulin absorption in diabetic type 1 patients. Method. In this research, we use continuous glucose monitoring (CGM) data to predict future blood glucose level using the previous data points. We compare two neural network techniques. We apply the optimal feedforward neural network and then propose optimal nonlinear autoregressive neural networks for blood glucose prediction 15–30 minutes earlier for diabetic type 1 patients. We validate the proposed model with 2 virtual subjects using their 24-hour blood glucose level data. These two case studies have been compiled from AIDA, i.e., the freeware mathematical diabetes simulator. Results. In the prediction horizon (PH) of 15 and 30 minutes, improved results have been shown for minimal inputs for blood glucose level of a particular subject. Root mean square error (RMSE) is used for performance calculation. For the optimal feedforward neural network, the RMSE is 0.9984 and 3.78 ml/dl, and for the optimal nonlinear autoregressive neural network, it reduces the RMSE to 0.60 and 1.12 ml/dl for 15 min and 30 min prediction horizons, respectively, for subject 1. Similarly, for subject 2 for the optimal feedforward neural network, RMSE is 1.43 and 3.51 ml/dl which is improved using the optimal autoregressive neural network to 0.7911 and 1.6756 ml/dl for 15 min and 30 min prediction horizons, respectively. Validation. We further validate our proposed model using UCI machine learning datasets (Abalone and Servo), and it shows improved results on that as well. Conclusion and Future Work. The proposed optimal nonlinear autoregressive neural network model performs better than the feedforward neural network model for these time series data. In the future, we intend to investigate a greater collection of AIDA scenarios and data that are real and influence other factors of BGLs.

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Section: Introductionmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

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Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Asad

Qamar

Abbas

2021

Journal of Healthcare Engineering

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“…We found 48 relevant publications 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 that presented a prediction algorithm published between 2013 and 2020 showing the recent increasing interest for this topic. Information on these algorithms is presented in appendices.…”

Section: Algorithms For Glucose and Hypoglycaemia Predictionmentioning

confidence: 99%

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

Diouri

Cigler

Vettoretti

et al. 2021

Diabetes Metabolism Res

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The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

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“…Hamdi, T. et al 2017, [19] proposed an Artificial Neural Network technique for an accurate blood glucose level prediction of Type1 Diabetes. To validate the proposed method, real Continuous Glucose Monitoring data of 12 patients were investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

Forestalling Growth Rate in Type Ii Diabetic Patients Using Data Mining and Artificial Neural Networks: An Intense Survey

Dubey¹,

Shrivastava²

2019

IJCET

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The race for urbanization and thirst for high living status leads to unhealthy life. As the result a rapid growth in number of diabetic patients in urban areas approaching to its deadline. In this situation it become a prime necessity for physicians and health workers to recognize accurate growth rate in number of diabetic patients. Artificial Neural Network is used as one of the artificial intelligent technique for forestalling growth rate of type II diabetic patients. Diabetes occurred due to increased level of glucose in blood. In this paper, an intense survey is done for the prediction of Type II diabetes using different Data Mining tools and Artificial Neural Network techniques, is presented. This survey is aimed to recognize and propose an effective technique for earlier prediction of the Type II diabetes. The data mining techniques like C4.5 Classifier, Support Vector Machine and K-Nearest Neighbour are compared for this work with Artificial Neural Network. As the results Artificial Neural Network found with a great accuracy of 89%.

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Artificial neural network for blood glucose level prediction

Cited by 23 publications

References 15 publications

Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

Forestalling Growth Rate in Type Ii Diabetic Patients Using Data Mining and Artificial Neural Networks: An Intense Survey

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