GC-54 COVID-19 Mortality Prediction using Machine Learning Techniques

Presenter Information

    Location

    https://ccse.kennesaw.edu/computing-showcase/cday-programs/spring2021program.php

    Streaming Media

    Event Website

    https://sites.google.com/view/covid-19-spring2021/home

    Document Type

    Event

    Start Date

    26-4-2021 5:00 PM

    Description

    In late 2019, SARS-CoV2 also known as COVID-19 was first identified in the city of Wuhan, China. This virus can infect a person and without showing any signs of sickness, can spread of COVID-19 unknowingly. The World Health Organization declared it a global pandemic in March 2020 because of its far-reaching effects in every part of the world. Scientists have been working to leverage technology to prevent spread, detection and vaccine development. With machine learning, models can predict which patient will most likely have a higher mortality rate. Using WEKA, a machine learning tool and a data set based on 95,000 Mexican patients with 20 clinical features, our research applies models to determine which has the most accuracy.
    Advisors(s): Dr. Seyedamin Pouriyeh
    Topic(s): Other (explain in the comments section)
    Machine Learning

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    Apr 26th, 5:00 PM

    GC-54 COVID-19 Mortality Prediction using Machine Learning Techniques

    https://ccse.kennesaw.edu/computing-showcase/cday-programs/spring2021program.php

    In late 2019, SARS-CoV2 also known as COVID-19 was first identified in the city of Wuhan, China. This virus can infect a person and without showing any signs of sickness, can spread of COVID-19 unknowingly. The World Health Organization declared it a global pandemic in March 2020 because of its far-reaching effects in every part of the world. Scientists have been working to leverage technology to prevent spread, detection and vaccine development. With machine learning, models can predict which patient will most likely have a higher mortality rate. Using WEKA, a machine learning tool and a data set based on 95,000 Mexican patients with 20 clinical features, our research applies models to determine which has the most accuracy.
    Advisors(s): Dr. Seyedamin Pouriyeh
    Topic(s): Other (explain in the comments section)
    Machine Learning

    https://digitalcommons.kennesaw.edu/cday/spring/graduatecapstone/4