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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on biomedical engineering Ročník 67; číslo 11; s. 3101 - 3124
Hlavní autoři:	Xie, Jinyu, Wang, Qian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!