Type2 Diabetes Classification from Short Photoplethysmogram Signal Using Multiple Domain Features and Machine Learning Techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-023-00287-7.pdf
Reference56 articles.
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3. American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care. 2019;42(Supplement 1):S13–28.
4. Araveeporn A. Comparing the linear and quadratic discriminant analysis of diabetes disease classification based on data multicollinearity. Int J Math Math Sci 2022. https://doi.org/10.1155/2022/7829795.
5. Avram R, Tison G, Kuhar P, Marcus G, Pletcher M, Olgin JE, Aschbacher K. Predicting diabetes from photoplethysmography using deep learning. J Am College Cardiol. 2019;73(9S2):16–16. https://doi.org/10.1016/s0735-1097(19)33778-7.
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1. Type-2 diabetes identification from toe-photoplethysmography using Fourier decomposition method;Neural Computing and Applications;2023-11-20
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