Title: Healthcare analytics with R and MongoDB using social media

Authors: Sonia Saini; S.P. Singh; Ruchi Agarwal

Addresses: Department of Computer Science and Engineering, BIT Mesra (Noida Campus), Ranchi, India ' Department of Computer Science and Engineering, BIT Mesra (Noida Campus), Ranchi, India ' Computer Application Department, JIMS Engineering Management Technical Campus, Greater Noida, India

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.

Keywords: R; MongoDB aggregation; map-reduce; streaming data; healthcare; machine learning; data classification; Java script object notation; JSON.

DOI: 10.1504/IJAIP.2021.113788

International Journal of Advanced Intelligence Paradigms, 2021 Vol.18 No.4, pp.552 - 567

Received: 20 Apr 2018
Accepted: 13 Nov 2018
Published online: 31 Mar 2021 *

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