Abstract
Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the World Health Organization. Intensive research is being carried out on artificial intelligence methods to help people with diabetes to optimize the way in which they use insulin, carbohydrate intakes, or physical activity. By predicting upcoming levels of blood glucose concentrations, preventive actions can be taken. Previous research studies using machine learning methods for blood glucose level predictions have mainly focused on the machine learning model used. Little attention has been given to the pre-processing of insulin and carbohydrate signals in order to mimic the human absorption processes. In this manuscript, a recurrent neural network (RNN) based model for predicting upcoming blood glucose levels in people with type 1 diabetes is combined with several carbohydrate and insulin absorption curves in order to optimize the prediction results. The proposed method is applied to data from real patients suffering type 1 diabetes mellitus (T1DM). The achieved results are encouraging, obtaining accuracy levels around 0.510 mmol/L (9.2 mg/dl) in the best scenario.
Highlights
Type 1 diabetes is among the most frequent chronic diseases in children and young people, having an important social and health impact and directly affecting the quality of life of the people who suffer from it
We propose and validate a new mechanism that incorporates physiological absorption models for insulin and carbohydrates into the pre-processing steps of a machine learning algorithm using a recurrent neural network based on long short term memory (LSTM)
The data for the insulin injections and carbohydrate intakes from all the participants will be pre-processed using the absorption curves selected for each experiment, as described in the previous section
Summary
Type 1 diabetes is among the most frequent chronic diseases in children and young people, having an important social and health impact and directly affecting the quality of life of the people who suffer from it. An exact mechanism for treating the disease is not known. This increases the importance of proposing and validating techniques that facilitate the monitoring and treatment of type 1 diabetics [1] and even the development of systems that allow the identification of patterns that can help finding the best treatment for each patient. Machine learning techniques and algorithms are proving to be able to build a solid foundation in many areas in general and for blood glucose prediction in particular [2,3]. The use of data from real patients combined with machine learning techniques and prediction algorithms allows us to monitor patients, predict possible drops or rises in blood glucose levels, and establish personal recommendations based on their particular and personal data. In order for the recommendations to be accurate, an appropriate combination of machine learning models and good quality data are required
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
