SUP Program: ENABLE
| 1:30-1:50 |
Destiny McClain, Besan Khader & Jasmine LucasPresentation Time: 1:30-1:50Home University: Winston-Salem State UniversityResearch Mentor: Shikha Yadav, Health SciencesProgram: ENABLEResearch Title: Factos Influencing the Mortality Rates of Breast CancerBreast Cancer is the 2nd most common Cancer in women in the U.S. Our project aims to utilize data mining and analyzation to explore the most common factors influencing the malignancy of breast cancer and its resulting mortality rates. We will explore the causative risk factors associated with the diagnosis of Breast Cancer through a correlational-descriptive design. Based on our extensive analysis, it was revealed that the mortality rates were heavily influenced by three variables: age at diagnosis, tumor size, and tumor stage. |
Factos Influencing the Mortality Rates of Breast Cancer | ENABLE |
| 1:55-2:15 |
Samia Khan, Gargi Dixit, Kailyn Sellers & Kayla BambergPresentation Time: 1:55-2:15Home University: Durham Technical Community CollegeResearch Mentor: Shikha Yadav, Health InformaticsProgram: ENABLEResearch Title: Data and Text Analysis of Potential Risk Factors of StrokeAbstract: The focus of our project was to utilize data analysis techniques to find a correlation between ten previously identified variables and the occurrence of stroke. Using these techniques, we were then able to draw conclusions on these variables and their influence on the likelihood of stroke. Another objective of the project was to use text mining to analyze documents from a comprehensive literature review and identify the most relevant terms and document similarity. |
Data and Text Analysis of Potential Risk Factors of Stroke | ENABLE |
| 2:20-2:40 |
Samuel Song, Jeongbin Pak, & Sydney LashPresentation Time: 2:20-2:40Home University: UNC-Chapel HillResearch Mentor: Shikha Yadav, Program CoordinatorProgram: ENABLEResearch Title: A Comprehensive Look at Covid-19 Data in Social MediaSocial media has played a prevalent role in conveying news, updated data, and public sentiment during the COVID-19 pandemic. Prior research has been conducted in evaluating the impact and the effectiveness of sharing pandemic news and data via social media, specifically gauging the credibility and relevance of the shared news. This study aims to perform a general sentiment analysis of social media users to evaluate their perspectives and their responses to COVID-19 related news and statistics. Text mining functions were used to process and filter out tweets from April 2020 – June 2021, sort them by word usage, as well as group them by sentiment and connotation. Furthermore, various data visualization functions were also implemented to obtain a comprehensive look at Covid-19 tweet data, recurrent phrases, and suggested sentiments. The results showed that more than 60% of analyzed tweets carried a neutral sentiment, followed by approximately 25% positive and 15% negative sentiments. These results suggest that most covid-19 related tweets on social media are non-opinionated or pertain to the sharing of data and news, and that general worldwide sentiment shows a positive throughout the pandemic. More research is needed to ascertain the relationship between data sharing and public sentiment, as well as accommodate for further data that arises as the pandemic continues. |
A Comprehensive Look at Covid-19 Data in Social Media | ENABLE |