Healthcare analytics with R and MongoDB using social media Online publication date: Wed, 31-Mar-2021
by Sonia Saini; S.P. Singh; Ruchi Agarwal
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 18, No. 4, 2021
Abstract: The recent advent of various social media platforms has opened new avenues of data collection and analysis. The unstructured social media data needs proper data classification for efficient healthcare analytics. R has latency and the latency is induced by need to 'load' 'offline' data files. This paper introduces a framework to create a model to offset the processing of streaming data in R. This paper demonstrates how the framework can work by analysing the frequency of the international classification of diseases (ICD-10) keywords as a part of healthcare analytics. The proposed framework offsets the work of resource intensive analytics tasks like dynamic querying, summation, aggregation, map-reduce by performing these at the NoSQL data store and R can use these pre-computed results to perform subsequent analytics. This paper also illustrates how efficiency can be achieved in processing streamed data in R by comparing processing times with and without use of the proposed model.
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