This paper is in the following e-collection/theme issue:

Foundations of AI (57) Applications of AI (246)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48340, first published .
Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Authors of this article:

Bohan Shi1, 2 Author Orcid Image ;   Satvinder Singh Dhaliwal3, 4, 5 Author Orcid Image ;   Marcus Soo1 Author Orcid Image ;   Cheri Chan6 Author Orcid Image ;   Jocelin Wong1 Author Orcid Image ;   Natalie W C Lam2 Author Orcid Image ;   Entong Zhou2 Author Orcid Image ;   Vivien Paitimusa2 Author Orcid Image ;   Kum Yin Loke2 Author Orcid Image ;   Joel Chin2 Author Orcid Image ;   Mei Tuan Chua6 Author Orcid Image ;   Kathy Chiew Suan Liaw6 Author Orcid Image ;   Amos W H Lim1 Author Orcid Image ;   Fadil Fatin Insyirah6 Author Orcid Image ;   Shih-Cheng Yen7 Author Orcid Image ;   Arthur Tay8 Author Orcid Image ;   Seng Bin Ang9, 10 Author Orcid Image
Article Authors Cited by (6) Tweetations (11) Metrics

Journals

  1. Chen X, Tao L, Wang Y. Association of dietary fiber intake with all-cause and cardiovascular mortality in diabetes and prediabetes. Diabetology & Metabolic Syndrome 2025;17(1) View
  2. Hu J, Ren L, Wang T, Yao P. Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients. Journal of Multidisciplinary Healthcare 2025;Volume 18:4643 View
  3. Kermani A, Esmaeili H. Photoplethysmography based non-invasive blood glucose estimation using systolic-diastolic framing MFCC features and machine learning regression. BioImpacts 2025;15:30589 View

Conference Proceedings

  1. Satter S, Kwon T, Kim K. 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). Non-Invasive Blood Glucose Estimation Based on Machine Learning Algorithms Using PPG Signals View
  2. Cui Y, Ma S, Wu X, Wang B. Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science. An LSTM-Based Model for Non-invasive Blood Glucose Prediction Utilizing BVP Signals View
  3. Dere B, Ogunkorode A, Akpor O, Oniyide A, Dere A, Bello K. 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON). Mobile and Wearable Solutions for Monitoring Diabetes: A Systematic Review View