Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things

Abstract

In recent times, Internet of Medical Things (IoMT) gained much attention in medical services and healthcare management domain. Since healthcare sector generates massive volumes of data like personal details, historical medical data, hospitalization records, and discharging records, IoMT devices too evolved with potentials to handle such high quantities of data. Privacy and security of the data, gathered by IoMT gadgets, are major issues while transmitting or saving it in cloud. The advancements made in Artificial Intelligence (AI) and encryption techniques find a way to handle massive quantities of medical data and achieve security. In this view, the current study presents a new Optimal Privacy Preserving and Deep Learning (DL)-based Disease Diagnosis (OPPDL-DD) in IoMT environment. Initially, the proposed model enables IoMT devices to collect patient data which is then preprocessed to optimize quality. In order to decrease the computational difficulty during diagnosis, Radix Tree structure is employed. In addition, ElGamal public key cryptosystem with Rat Swarm Optimizer (EIG-RSO) is applied to encrypt the data. Upon the transmission of encrypted data to cloud, respective decryption process occurs and the actual data gets reconstructed. Finally, a hybridized methodology combining Gated Recurrent Unit (GRU) with Convolution Neural Network (CNN) is exploited as a classification model to diagnose the disease. Extensive sets of simulations were conducted to highlight the performance of the proposed model on benchmark dataset. The experimental outcomes ensure that the proposed model is superior to existing methods under different measures.