TrCN-HDC: Enhancing patient security with graphical authentication and cloud-assisted cardiac monitoring

Heart disease (HD) remains the leading cause of mortality worldwide, emphasizing the need for early detection and accurate diagnosis to improve patient outcomes. The integration of Internet of Things (IoT) technology in healthcare has enabled real-time data collection through smart wearable devices, facilitating continuous monitoring and early identification of HD. However, the low survival rates of sudden heart attacks highlight the necessity for a secure and intelligent patient monitoring system.

To address this, we propose TrCN-HDC, a Transformer with Capsule Network-based framework designed for IoT-assisted heart disease diagnosis and prediction. The framework ensures patient security and privacy by implementing biometric authentication with the SHA-512 algorithm, securing access to medical data. Patient information is collected through wearable sensors and undergoes pre-processing, including missing value replacement, data normalization, and noise reduction, to enhance data quality. TrCN-HDC framework processes the refined data through Ascended Extensive ResNet (AE-ResNet) for deep feature extraction, followed by Hummingbird Optimization Algorithm (HBOA) for feature selection. The selected features are further refined using the Lightweight Wave Transformer (LW2T) for improved representation before final heart disease classification using Capsule Network (CapNet).

The model's performance is evaluated using accuracy, disease prevalence (DP), positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, F1-score, and ROC curve analysis. Conclusions- Experimental results demonstrate that TrCN-HDC outperforms existing models, providing a highly accurate, secure, and efficient heart disease detection system in modern smart healthcare environments.