SMLBT: secure machine learning and blockchain-based telemedicine model for the remote areas of developing countries

Abstract

A reliable data safety model is currently an urgent demand for the healthcare system across the world, especially for people dwelling in rural areas, and this domain requires top-notch security. Telemedicine services can serve rural communities with appropriate medical guidance, but patient data security is still in question. In this work, we propose a telemedicine system using blockchain technology that can ensure the data security of patients from remote and rustic regions of any country. Based on the literature, we present a medical information system, which includes data pre-processing and cleaning. To create prediction models, we propose supervised and unsupervised machine learning (ML) technologies to analyse patient narratives and electronic medical records (EMR). These ML approaches will initiate early diagnostics to transform the system into a more scalable format. Furthermore, this model may put forward economic, social, and technologically enhanced medical advantages to the citizens of rural areas.

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