Data Analysis to Analyze Mental Health in Global Pandemic
| 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.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 | |
| dc.identifier.uri | https://hdl.handle.net/1920/12894 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | Suicide Prevention | |
| dc.subject | Python | |
| dc.subject | Post-COVID | |
| dc.title | Data Analysis to Analyze Mental Health in Global Pandemic | |
| dc.type | Working Paper |