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Machine learning approach for COVID-19 crisis using the clinical data

NOPR - NISCAIR Online Periodicals Repository

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Field Value
 
Title Machine learning approach for COVID-19 crisis using the clinical data
 
Creator Kumar, NRP
Shetty, NS
 
Subject Accentuation lemmatisation
Bagging
Dyspnoea
 
Description 602-605
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.
 
Date 2020-09-30T05:28:45Z
2020-09-30T05:28:45Z
2020-10
 
Type Article
 
Identifier 0975-0959 (Online); 0301-1208 (Print)
http://nopr.niscair.res.in/handle/123456789/55364
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source IJBB Vol.57(5) [October 2020]