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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on biomedical engineering Vol. 67; no. 11; pp. 3101 - 3124
Main Authors:	Xie, Jinyu, Wang, Qian
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!