Glucose classification and prediction system with neural networks

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Varela Lorenzo, Alejandro and Delgado Gutierrez, Alvaro (2020) Glucose classification and prediction system with neural networks. [Trabajo Fin de Grado]

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Abstract

Glucose levels prediction is a difficult task commonly faced by people with diabetes, a chronic health condition that affects how a human body synthesizes food. People with diabetes must have an exhaustive control of the levels of sugar in the bloodstream in order to manage insulin intakes, a procedure that is usually done manually and without enough accuracy. Levels of glucose in a human body depends on a lot of different factors, so the risk of doing miscalculations is always taken by the patient. Nowadays, using new technologies such as Artificial Intelligence (AI) or Machine Learning (ML), these calculations can be supported and eased by the application of prediction systems. The field of AI and ML is very large, providing scientists with several different tools to create forecasting algorithms. During this project, we are going to focus on the creation and use of Neural Networks (NN) for glucose level prediction. To create this systems, we are exploring different types of Neural Networks (NN), ranging from regular numeric NN to graphic NN applications, which are vastly known in the world of data scientists. However, these algorithms are always a difficult and hidden process for the real users, diabetes patients, so in order to make a higher impact on people affected by this disease, we will create an online application that will share all the power of NNs with the final patients, using a clear and intuitive user interface. In the meantime, patients will collaborate on the NN training process, as long as data provided by those using the application will allow us to develop a more heavily trained NN, thus improving its effectiveness in glucose levels forecasting. Referring to the numerical NN and according to the results, we can state that their performance for 30 and 60 minutes prediction is quite accurate and, for 90 and 120 minutes prediction, it throws promising results. Instead, for the graphical NN, even though the approach is interesting and the studies are showing us that the technology is very powerful, it needs much more investigation until we get good results.


Item Type:Trabajo Fin de Grado
Additional Information:

Trabajo Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Arquitectura de Computadores y Automática, Curso 2019/2020.

Directors:
Directors
Hidalgo Pérez, José Ignacio
Uncontrolled Keywords:Diabetes mellitus, Glucose levels prediction, Neural networks, Forecasting, Online service, Machine learning, NVIDIA Digits.
Subjects:Sciences > Computer science
Título de Grado:Grado en Ingeniería Informática
ID Code:68286
Deposited On:20 Oct 2021 14:58
Last Modified:20 Oct 2021 14:58

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