Food recommendation using machine learning for physical activities in patients with type 1 diabetes
Year: 2019
Type: article
Abstract: Physical activities have a significant impact on blood glucose homeostasis of patients with type 1 diabetes.\nRegular physical exercise provides many proven health benefits and is recommended as part of a healthy lifestyle.\nHowever, one of the main side effects of physical activities is hypoglycemia (low blood glucose). Fear of\nhypoglycemia generally leads to the patients not participating in physical activities. This paper shows a proof of\nconcept that machine learning can be used to create a personalized food recommendation system for patients with\ntype 1 diabetes. Machine learning algorithms were designed to improve glycemic control and reduce the\novercompensation of carbohydrate. First, a personalized model based on feedforward neural networks is\ndeveloped to predict the blood glucose outcome during and after physical activities. Based on the personalized\nmodel and reinforcement learning, optimal food intakes will be recommended to the patient. Simulation results\nshow that the proposed methodology has successfully maintained the blood glucose in the healthy range on a\ntype 1 diabetes simulator during physical activities.
Institution: UiT The Arctic University of Norway
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3009301559