Selecting A Better Classifier Using Machine Learning For COVID-19
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Date
2019
Authors
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Journal ISSN
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Publisher
UMT, Lahore
Abstract
Now a day’s world is confronting a severe issue identified as Coronavirus. Its officially declare as
COVID-19. In this infection we don’t use clinically approved vaccines and medicines. Antibiotics
give a relief to the effected patients because proper vaccination is not discovered. COVID-19 has
resemblance like pervious infectious diseases such as Middle East Respiratory Syndrome (MERS)
and Sever Acute Respiratory Syndrome (SARS). World need quick and rapid precautionary
measures to handle this outbreak. Wuhan, Chinese city is the hub of this infection. To achieve the
outcomes and future forecasting of COVID-19, we analyze the records and datasets of COVID-19
through Machine Learning algorithms. For this purpose, we used various algorithms to construct
classifiers such as: Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (K-NN),
Naïve Bayes and Random Forecast. These algorithms apply on different software Python. In our
research we discussed two types of classification: Binary and Multinomial. Support Vector
Machine and Decision Tree give us precise results. Other classifier models gave satisfactory
outcomes. Above algorithms directly apply on datasets in Python and programming Language.
The outcomes may be helping to predict the future circumstances of COVID-19.