Artificial Rabbits Optimizer with Deep Learning Model for Blockchain-Assisted Secure Smart Healthcare System

Smart healthcare is based on the electronic health and medical histories of residents, combined with information technology (IT) which can be used to construct a variety of systems including humanised health management systems and convenient medical service systems. The transparency, traceability, decentralization and security of BC technology and machine learning (ML) will enable the medical sector to upgrade and optimise different forms of quality and service. Therefore, this study introduces an artificial rabbit optimizer with deep learning for Blockchain Assisted Secure Smart Healthcare System (ARODL-BSSHS) technique. The presented ARODL-BSSHS technique designs a new healthcare monitoring technique by using blockchain (BC) technology and classifies the presence of malicious activities in the healthcare system, and takes needed actions to predict the disease. For intrusion detection, the ARODL-BSSHS technique exploits the ARO algorithm with Hop field neural network (IHNN) model. On the other hand, the ARODL-BSSHS technique applies a deep extreme learning machine (DELM) model for disease detection purposes. Finally, the heap-based optimization (HBO) technique is exploited as a hyperparameter optimizer for the DELM model. The ARODL-BSSHS technique involves BC technology for the secure transmission of healthcare data. A series of simulations were carried out on benchmark datasets: heart disease and NSL-KDD database for examining the performance of the ARODL-BSSHS technique. The experimental values highlighted that the ARODL-BSSHS method obtains superior performance than other approaches.

@article{Khubrani2023,
  title     = {Artificial Rabbits Optimizer with Deep Learning Model for Blockchain-Assisted Secure Smart Healthcare System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Mousa Mohammed Khubrani},
  doi       = {10.14569/IJACSA.2023.0140998},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140998}
}