Blood Glucose Monitoring Using Non-Invasive Features of Wearable Devices and Machine Learning-Reference-Cited by-同舟云学术

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Blood Glucose Monitoring Using Non-Invasive Features of Wearable Devices and Machine Learning

Published:2024-03-22 Issue: Volume:448 Page:409-413
ISSN:
Container-title:Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
language:
Short-container-title:

Author:

Zhang Jiawen¹^ORCID,Huang Xiaoyan²^ORCID,Chen Qian¹^ORCID

Affiliation:

1. Polytechnic Institute, Zhejiang University, Hangzhou, China, China

2. College of Electrical Engineering, Zhejiang University, Hangzhou, China, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3654823.3654897

Reference16 articles.

1. International Diabetes Federation. IDF Diabetes Atlas, 10th edn. Brussels: International Diabetes Federation, 2021.

2. American Diabetes Association. Guide to diagnosis and classification of diabetes mellitus and other categories of glucose intolerance. Diabetes Care 1997, 20, S21.

3. Marateb H R, Mansourian M, Faghihimani E, A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin[J]. Computers in biology and medicine, 2014, 45: 34-42.

4. Accuracy and user performance evaluation of the Contour® Next Link 2.4 blood glucose monitoring system[J];Bailey T;Clinica Chimica Acta,2015

5. Smith J L. The pursuit of noninvasive glucose: hunting the deceitful turkey[J]. Revised and Expanded, copyright, 2015.

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