Open Access

ARTICLE

PPG Based Digital Biomarker for Diabetes Detection with Multiset Spatiotemporal Feature Fusion and XAI

Mubashir Ali^1,2, Jingzhen Li¹, Zedong Nie^1,*

1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
2 University of Chinese Academy of Sciences, Beijing, 101408, China

* Corresponding Author: Zedong Nie. Email:

(This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)

Computer Modeling in Engineering & Sciences 2025, 145(3), 4153-4177. https://doi.org/10.32604/cmes.2025.073048

Received 09 September 2025; Accepted 29 October 2025; Issue published 23 December 2025

Abstract

Diabetes imposes a substantial burden on global healthcare systems. Worldwide, nearly half of individuals with diabetes remain undiagnosed, while conventional diagnostic techniques are often invasive, painful, and expensive. In this study, we propose a noninvasive approach for diabetes detection using photoplethysmography (PPG), which is widely integrated into modern wearable devices. First, we derived velocity plethysmography (VPG) and acceleration plethysmography (APG) signals from PPG to construct multi-channel waveform representations. Second, we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal, statistical, and nonlinear features with recursive feature elimination and deep feature extraction using a one-dimensional statistical convolutional neural network (1DSCNN). Finally, we developed an interpretable diabetes detection method based on XGBoost, with explainable artificial intelligence (XAI) techniques. Specifically, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were employed to identify and interpret potential digital biomarkers associated with diabetes. To validate the proposed method, we extended the publicly available Guilin People’s Hospital dataset by incorporating in-house clinical data from ten subjects, thereby enhancing data diversity. A subject-independent cross-validation strategy was applied to ensure that the testing subjects remained independent of the training data for robust generalization. Compared with existing state-of-the-art methods, our approach achieved superior performance, with an area under the curve (AUC) of 80.5±15.9%, sensitivity of 77.2±7.5%, and specificity of 64.3±18.2%. These results demonstrate that the proposed approach provides a noninvasive, interpretable, and accessible solution for diabetes detection using PPG signals.

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Keywords

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