DefinePK

Abstract

These days, the usage of blockchain with machine learning to optimise data validation in terms of transparency, validity, and immutability has been increasing daily. Therefore, many complex applications, such as healthcare and related disease processes, have recently required the implementation of many remote resources in a transparent form. The blockchain provides real-time security validation based on proof of work validation schemes. To understand the dynamic situation of blockchain, mainly machine learning implemented for the decision and improve the efficiency of the security. However, there are many limitations when using blockchain technology with machine learning. Therefore, to cope with this issue, a novel blockchain proof of validation scheme based on explainable AI for healthcare applications is needed to process the decision of blockchain with machine learning in a more explainable way. We present the blockchain proof of work validation explainable AI (PoWV-XAI) to control the delay, energy, cost and security dynamic issues compared to existing blockchains with machine learning algorithms. The proposed PoWV-XAI algorithm suggested different metaheuristic schemes and supported the explainability of healthcare workload execution on other nodes, such as local and server. Simulation results show that the proposed PoWV-XAI is more explainable, and all decisions, such as processing delay, validation, security, energy, and cost, are explainable compared to existing blockchain methods.