Blockchain-enabled hybrid CNN–Transformer framework with Integrated Gradients for Parkinson’s disease detection

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor impairments such as tremor, rigidity, bradykinesia, and gait instability. Accurate and early detection remains challenging due to the complex temporal nature of wearable sensor signals, limited interpretability of deep learning models, and concerns regarding medical data integrity. This study proposes a novel Blockchain-Enabled Hybrid CNN–Transformer framework with Integrated Gradients for PD detection using smartwatch-based inertial sensor data. The CNN module extracts local motion features, while the Transformer captures long-range temporal dependencies within movement signals. To enhance transparency, Integrated Gradients is employed to identify the most influential signal regions contributing to model predictions. In addition, a lightweight blockchain layer is incorporated to provide tamper-resistant storage and traceability of diagnostic outcomes. Experimental evaluation on the PADS dataset achieved 97.29% accuracy, 97.14% precision, 97.29% recall, 97.12% F1-score, and an AUC of 0.9869. The results demonstrate a reliable, interpretable, and secure framework for wearable-based Parkinson’s disease detection.