Selecting A Better Classifier Using Machine Learning For COVID-19

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dc.contributor.authorMUHAMMAD IMRAN
dc.date.accessioned2025-10-02T14:49:45Z
dc.date.available2025-10-02T14:49:45Z
dc.date.issued2019
dc.description.abstractNow 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.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/7787
dc.language.isoen
dc.publisherUMT, Lahore
dc.titleSelecting A Better Classifier Using Machine Learning For COVID-19
dc.typeThesis
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