[Retracted] Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling

Abstract

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.

Wearable Photoplethysmography for Cardiovascular Monitoring

“…Additional physiological parameters have been estimated from the PPG although these techniques are relatively novel. Blood glucose level, widely used for diabetes self-management, has been estimated from PPG pulse wave shape [ 152 ], [ 176 ], and the pulse wave has been used to classify patients as diabetic or not [ 177 ]. Cardiac output, monitored in peri-operative and critical care settings, has been estimated from pulse wave shape and low-frequency PPG variations [ 6 ], [ 178 ].…”

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

“…Qawqzeh et al [15] proposed a logistic regression model based on photoplethysmogram analysis for diabetes classification. ey used 459 patients' data for training and 128 data points to test and validate the model.…”

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

“…The combination of PPG data collected by near-infrared sensors with artificial intelligence (AI) makes it possible to predict BGL. Machine learning (ML) or deep learning (DL) approaches are equally good at predicting BGL using PPG [14,[38][39][40][41][42][43][44]. A common challenge in implementing ML or DL is limited test samples.…”