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Data Analysis to Analyze Mental Health in Global Pandemic

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dc.contributor.author Antonova, Helen
dc.contributor.author Liao, Spencer
dc.contributor.author Chandra Sai Pamidimukkala, Eswara
dc.date.accessioned 2022-06-08T16:14:45Z
dc.date.available 2022-06-08T16:14:45Z
dc.date.issued 2022-06-08
dc.identifier.uri http://hdl.handle.net/1920/12894
dc.description.abstract According to the 2021 Report from the World Health Organization (WHO), more than 700,000 people have taken their life. Suicide can be prevented but so far most of the efforts to do so have fallen short. However, the use of machine learning and artificial intelligence offers new opportunities to increase the accuracy level of prediction and aid the goal of suicide prevention. This paper reviews literature concerning the machine learning methods used to help identify various risk factors and help prevent suicide. This paper also presents our research and analysis findings which were used to identify various suicide risk factors and additional analysis of whether there are any correlations or variations in the risk factors from pre and post-pandemic datasets regarding suicide rates. This is especially important during times of high stress, such as a worldwide pandemic and quarantine. The dataset(s) obtained from WHO suggest that high levels of risk factor identification are possible, and this paper and the analysis serve as supporting research and guide to aid in the continued ambitious goal of suicide prevention worldwide en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject Suicide Prevention en_US
dc.subject Python en_US
dc.subject Post-COVID en_US
dc.title Data Analysis to Analyze Mental Health in Global Pandemic en_US
dc.type Working Paper en_US


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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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