[Retracted] Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling
Open accessAbstract
In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression‐based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.
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The pulse wave has been used to classify patients as diabetic or not.
“the pulse wave has been used to classify patients as diabetic or not”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [177]
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Cites
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Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250 · see citing works
Susana, E., Ramli, K., Murfi, H., & Apriantoro, N. H. (2022) · Information · doi:10.3390/info13020059 · Cited by 59 · see citing works
- Authors
- Qawqzeh, Y. K., Bajahzar, A. S., Jemmali, M., Otoom, M. M., & Thaljaoui, A.
- Affiliations
- Majmaah University (SA)
- Corresponding author
- Qawqzeh, Yousef K.
- Venue
- BioMed Research International
- Publisher
- Wiley
- Published
- 2020-01
- Volume
- 2020
- Issue
- 1
- Pages
- 3764653-3764653
- ISSN
- 1110-7243 (ISSN-L)
- 2314-6133
- 2314-6141
- PMC
- PMC7439205
- OpenAlex
- W3048779574
- Cited by
- 71
- References
- 16
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 14 · 2023: 20 · 2022: 24 · 2021: 3
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- Open access
- hybrid
- doi.org
- Locations
- BioMed Research International (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Majmaah University (1439-4)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- diabetes classification, photoplethysmogram, logistic regression, diabetes prediction, B/A ratio, non-invasive diagnosis
- MeSH terms
- Adult, Aged, Diabetes Mellitus, Female, Humans, Male, Middle Aged, Logistic Models, Photoplethysmography
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