Open AccessComparison of algorithms for the prediction of glucose levels in patients with diabetes
Author(s) -
Daniel Arturo Olivares Vera,
David Asael Gutiérrez Hernández,
Marco Antonio Camacho Escobar,
Claudia Lara-Rendón,
Dulce A. Velázquez-Vázquez
Publication year - 2021
Publication title -
nova scientia
Language(s) - English
Resource type - Journals
ISSN - 2007-0705
DOI - 10.21640/ns.v13i26.2752
Subject(s) - autoregressive integrated moving average , univariate , autoregressive model , series (stratigraphy) , artificial neural network , statistics , mathematics , value (mathematics) , algorithm , mean squared prediction error , time series , diabetes mellitus , autoregressive–moving average model , econometrics , computer science , artificial intelligence , medicine , multivariate statistics , endocrinology , biology , paleontology
This work presents a comparison between two algorithms for the prediction of glucose levels in diabetic patients by using a univariate time series. The algorithms are applied to the history of fasting glucose levels to predict the five following values. The comparison is performed between 1) The Autoregressive Neural Networks (ARNN) and 2) The autoregressive integrated moving average (ARIMA) models. A total of 70 series are analyzed, and we show that the results obtained for the ARIMA model have error percentages higher than 25% of the predicted value to the expected value. In contrast, in 73% of the cases, the percentage error was less than 25% for the Autoregressive Neural Networks.