Comparison of algorithms for the prediction of glucose levels in patients with diabetes
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Keywords

Neural Network Autoregressive
ARIMA
Univariate Time Series
Prediction
Glucose
Diabetic.
diabetes
chronic degenerative disease
insulin
hyperglycemia
predictive algorithm
neural networks
black-box models Red Neuronal Autoregresiva
ARIMA
Series de Tiempo Univariadas
Predicción
Glucosa
Diabético
diabetes
enfermedad crónico degenerativa
insulina
hiperglucemia
algoritmo predictivo
redes neuronales
modelos de caja negra

How to Cite

Olivares Vera, D. A., Gutiérrez Hernández, D. A., Escobar Acevedo, M. A., Lara Rendón, C. M., & Velázquez Velázquez, D. A. (2021). Comparison of algorithms for the prediction of glucose levels in patients with diabetes. Nova Scientia, 13(26). https://doi.org/10.21640/ns.v13i26.2752

Abstract

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.

https://doi.org/10.21640/ns.v13i26.2752
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References

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