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Volume 10
Issue 22
10.3390/app10228037
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Article

Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities

by
Phuong Ngo
1,*,
Miguel Tejedor
2,*,
Maryam Tayefi
1,
Taridzo Chomutare
1 and
Fred Godtliebsen
1,3
1
Norwegian Centre for E-health Research, University Hospital of Northern Norway, 9019 Tromsø, Norway
2
Department of Computer Science, Faculty of Science and Technology, UiT The Arctic University of Norway, 9019 Tromsø, Norway
3
Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway, 9019 Tromsø, Norway
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(22), 8037; https://doi.org/10.3390/app10228037
Submission received: 7 October 2020 / Revised: 6 November 2020 / Accepted: 9 November 2020 / Published: 12 November 2020
(This article belongs to the Special Issue Medical Artificial Intelligence)
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Abstract

Background. Since physical activity has a high impact on patients with type 1 diabetes and the risk of hypoglycemia (low blood glucose levels) is significantly higher during and after physical activities, an automatic method to provide a personalized recommendation is needed to improve the blood glucose management and harness the benefits of physical activities. This paper aims to reduce the risk of hypoglycemia and hyperglycemia (high blood glucose levels), and empowers type 1 diabetes patients to make decisions regarding food choices connected with physical activities. Methods. Traditional and Bayesian feedforward neural network models are developed to provide accurate predictions of the blood glucose outcome and the risks of hyperglycemia and hypoglycemia with uncertainty information. Using the proposed models, safe actions that minimize the risk of both hypoglycemia and hyperglycemia are provided as food recommendations to the patient. Results. The predicted blood glucose responses to the optimal and safe food recommendations are significantly better and safer than by taking random food. Conclusions. Simulations conducted on the state-of-the-art UVA/Padova simulator combined with Brenton’s physical activity model show that the proposed methodology is safe and effective in managing blood glucose during and after physical activities.
Keywords: type-1 diabetes; machine learning; feedforward neural networks; Bayesian neural networks; physical activities type-1 diabetes; machine learning; feedforward neural networks; Bayesian neural networks; physical activities

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MDPI and ACS Style

Ngo, P.; Tejedor, M.; Tayefi, M.; Chomutare, T.; Godtliebsen, F. Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities. Appl. Sci. 2020, 10, 8037. https://doi.org/10.3390/app10228037

AMA Style

Ngo P, Tejedor M, Tayefi M, Chomutare T, Godtliebsen F. Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities. Applied Sciences. 2020; 10(22):8037. https://doi.org/10.3390/app10228037

Chicago/Turabian Style

Ngo, Phuong, Miguel Tejedor, Maryam Tayefi, Taridzo Chomutare, and Fred Godtliebsen. 2020. "Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities" Applied Sciences 10, no. 22: 8037. https://doi.org/10.3390/app10228037

APA Style

Ngo, P., Tejedor, M., Tayefi, M., Chomutare, T., & Godtliebsen, F. (2020). Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities. Applied Sciences, 10(22), 8037. https://doi.org/10.3390/app10228037

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