Abstract
Distributed transactions in e-Healthcare and the evaluation of medical data have become an active research area of information technology that delivers medical records management and optimization without manually visualizing the computational loss. The increased use of e-Healthcare applications for availing medical services requires efficient computation during the processing of medical transactions and preservation through intelligent measurement analysis. Medical industries often involve and aim for the smooth application of medical transmission of demanding services. Thus, there are significant requirements for calculating loss during optimization and management in the distributed private network. In this paper, we contribute to two different objectives. First, we propose a machine learning-based stochastic gradient descent method for managing medical records and optimizing day-to-day transactions of e-Healthcare applications. This approach evaluates the loss of medical features during computation and enables optimized details of data transmission. Secondly, a blockchain-distributed E-Healthcare novel and a secure serverless architecture are proposed for the medical industry to protect transactions and preserve immutable storage. The simulation result shows the proposed system computations, such as loss = 0.7 (7%), learning-rate = goldilocks, ledger optimization =0.23 (23%), transmission power =-18 dBm, jitter = 32 ms, delay =90 ms, throughput = 170 bytes, duty-cycle and delivery = 0.10(10%), and calculate dynamic response.