Research on type 2 diabetes prediction algorithm based on photoplethysmography.
10.7507/1001-5515.202501006
- Author:
Mingying HU
1
;
Quanyu WU
1
;
Yifan CAO
1
;
Jin CAO
1
;
Yifan ZHAO
1
;
Lin ZHANG
1
;
Xiaojie LIU
1
Author Information
1. School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, P. R. China.
- Publication Type:Journal Article
- Keywords:
Data augmentation;
Generative adversarial networks;
Photoplethysmography;
Self-attention;
Type 2 diabetes
- MeSH:
Photoplethysmography/methods*;
Diabetes Mellitus, Type 2/diagnosis*;
Humans;
Algorithms;
Neural Networks, Computer;
Signal Processing, Computer-Assisted;
Prediction Algorithms
- From:
Journal of Biomedical Engineering
2025;42(5):1005-1011
- CountryChina
- Language:Chinese
-
Abstract:
To address the current issues of data imbalance and scarcity in photoplethysmography (PPG) data for type 2 diabetes mellitus (T2DM) prediction, this study proposes an improved conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP). The algorithm integrated gated recurrent unit (GRU) networks and self-attention mechanisms to construct a generator, aiming to produce high-quality PPG signals. Various data augmentation methods, including the improved CWGAN-GP, were employed to expand the PPG dataset, and multiple classifiers were applied for T2DM prediction analysis. Experimental results showed that the model trained on data generated by the improved CWGAN-GP achieved the optimal prediction performance. The highest accuracy reached 0.895 0, and compared with other data enhancement methods, this approach exhibited significant advantages in terms of precision and F1-score. The generated data notably enhances the accuracy and generalization ability of T2DM prediction models, providing a more reliable technical basis for non-invasive early T2DM screening based on PPG signals.
