Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55364
Title: Machine learning approach for COVID-19 crisis using the clinical data
Authors: Kumar, NRP
Shetty, NS
Keywords: Accentuation lemmatisation;Bagging;Dyspnoea
Issue Date: Oct-2020
Publisher: NISCAIR-CSIR, India
Abstract: We try to identify the impact of innovation headways and its rapid affect in each field of life, be it clinical or some other field; computerized reasoning deployed the prominent approach for indicating the authenticated outcomes in the field of medical services through its dynamic nature in investigating the information. COVID-19 has influenced all the nations around the globe in a short period of time duration; Individuals everywhere over the world are defenceless, against its results in the future. It is necessary to build up a control framework that will distinguish the Covid. One of the answers for control the flow ruin can be the conclusion of illness with the assistance of different artificial intelligence instruments.
In this paper, we ordered literary clinical reports into four classes by utilizing old style and troupe AI calculations. Feature designing was performed utilizing procedures like Term recurrence/reverse archive recurrence (TF/IDF), Bag of words (BOW) and report length. These highlights were provided to customary and troupe AI classifiers. Calculated relapse and Multinomial Naive Bayes demonstrated preferred outcomes over other ML calculations by having 96.2% testing exactness. In the future intermittent neural organization can be utilized for better exactness.
Page(s): 602-605
URI: http://nopr.niscair.res.in/handle/123456789/55364
ISSN: 0975-0959 (Online); 0301-1208 (Print)
Appears in Collections:IJBB Vol.57(5) [October 2020]

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