Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison With Classical Time-Series Models

Objective: This paper aims to compare the performance of several commonly known machine-learning (ML) models versus a classic Autoregression with Exogenous inputs (ARX) model in the prediction of blood glucose (BG) levels using time-series data of patients with Type 1 diabetes (T1D). Methods: The ML...

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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 67; H. 11; S. 3101 - 3124
Hauptverfasser:	Xie, Jinyu, Wang, Qian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Benchmark Blood Blood glucose Convolution Data models Deep learning deep neural network Diabetes Diabetes mellitus (insulin dependent) Glucose Learning algorithms Machine learning Oscillations Performance measurement Prediction algorithms Predictions Predictive models Recursive methods Regression analysis Regression models Root-mean-square errors Sugar Time series type 1 diabetes
ISSN:	0018-9294, 1558-2531, 1558-2531
Online-Zugang:	Volltext
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