Reinforcement learning for medical information processing over heterogeneous networks

Fog computing is an emerging trend in the healthcare sector for the care of patients in emergencies. Fog computing provides better results in healthcare by improving the quality of services in the heterogeneous network. The transmission of critical multimedia healthcare data is required to be transferred in real-time for saving the lives of patients using better quality networks. The main objective is to improve the quality of service over a heterogeneous network by reinforcement learning-based multimedia data segregation (RLMDS) algorithm and Computing QoS in Medical Information system using Fuzzy (CQMISF) algorithm in fog computing. The proposed algorithms works in three phase’s such as classification of healthcare data, selection of optimal gateways for data transmission and improving the transmission quality with the consideration of parameters such as throughput, end-to-end delay and jitter. Proposed algorithms used to classify the healthcare data and transfer the classified high-risk data to end-user with by selecting the optimal gateway. To performance validation, extensive simulations were conducted on MATLAB R2018b on different parameters like throughput, end-to-end delay, and jitter. The performance of the proposed work is compared with FLQoS and AQCA algorithms. The proposed CQMISF algorithm achieves 81.7% overall accuracy and in comparison to FLQoS and AQCA algorithm, the proposed algorithms achieves the significant improvement of 6.195% and 2.01%.