Optimized IoT ‐Based Surveillance System for Secure Management of Electronic Health Records

ABSTRACT Data security in Electronic health records (EHRs) plays a crucial role in the healthcare system because the EHRs contain sensitive information about patients' personal and medical data, making them a prime target for security breaches and unauthorized access. EHRs are often accessed and updated by many healthcare professionals, so robust data security measures are necessary to control and monitor access to patient records. To safeguard patient data against unauthorized access, numerous deep learning (DL) approaches have been introduced. However, many of these methods face significant limitations, such as low detection precision, excessive computational demands, and susceptibility to overfitting. In response to these challenges, this study introduces a novel hybrid model that combines Recurrent Backpropagation Learning (RBPL) with a Bi‐directional Long‐Term Deep Belief Network (BLDBN). This integrated approach aims to strengthen the security of Electronic Health Records (EHRs) within IoT‐enabled cloud infrastructures. The model processes input data sourced from both the Healthcare Dataset and the IoT Healthcare Security Dataset, incorporating preprocessing techniques to improve data quality. The RBPL‐BLDBN utilizes a feature selection module for selecting the more significant features. The selected features improve the model's efficiency by reducing computational complexity and improving accuracy. The RBPL‐BLDBN technique designs an RBPL approach to handle time‐dependent data, leveraging its recurrent nature to track sequential health records and detect anomalies or unusual behavior patterns. Furthermore, the BLDBN component captures hierarchical features across multiple layers and enhances the model's ability to detect intricate patterns in healthcare data. The proposed RBPL‐BLDBN technique not only enhances the security of healthcare data but also detects and prevents unauthorized access or anomalies in real time. Extensive evaluation of the RBPL‐BLDBN framework across multiple performance indicators reveals its superior capability in healthcare data protection. The model achieves impressive scores, attaining 98.84% in accuracy, 98.55% in precision, and 97.87% in specificity, which highlights its efficiency and robustness. The comparative analysis further demonstrates that the RBPL‐BLDBN not only conserves computational resources but also surpasses the performance of several existing security models in the domain.