Swarm Spider Optimization with Machine Learning based Disease Detection on Blockchain Assisted Healthcare Environment

Abstract

Blockchain (BC) and artificial intelligence (AI) technologies are innovations in healthcare field. Over the last decade, BC technology was developing at a considerable rate of growth. It presented as the strength of cryptocurrencies like Bitcoin, it quickly establish its application in other domains due to its privacy and security features. BC was utilized in the medical field for various drives involving maintenance, secured data logging, and transactions utilizing smart contracts. An excellent work is conducted to make BC smart, with the combination of AI to synthesize the better features of two technologies. This study designs a new Swarm Spider Optimization with Machine Learning based Disease Detection on Blockchain Assisted Healthcare Environment (SSOML-DDBHE) technique. The presented SSOML-DDBHE technique exploits BC technology for accurate identification of diseases in the healthcare environment. In the SSOML-DDBHE technique, the SSO algorithm is used for selecting feature subsets. For disease detection, the SSOML-DDBHE technique employs extreme learning machine (ELM) model. The ELM Parameters are adjusted using the Bayesian optimization (BO) algorithm at last. To achieve security, blockchain technology is used. The SSOML-DDBHE method is being experimentally analyzed using a benchmark dataset for heart disease. The comprehensive outcome highlighted the superior performance of the SSOML-DDBHE algorithm over recent approaches.