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- All Subjects: Machine Learning
- Creators: School of Mathematical and Statistical Sciences
This study utilized a literature review and an analysis of Google Trends and Google News data in order to investigate the coverage that American men’s soccer gets from the media compared to that given to other major American sports. The literature review called upon a variety of peer-reviewed, scholarly entries, as well as journalistic articles and stories, to holistically argue that soccer receives short-sighted coverage from the American media. This section discusses topics such as import substitution, stardom, and American exceptionalism. The Google analysis consisted of 30 specific comparisons in which one American soccer player was compared to another athlete playing in one of America’s major sports leagues. These comparisons allowed for concrete measurements in the difference in popularity and coverage between soccer players and their counterparts. Overall, both the literature review and Google analysis yielded firm and significant evidence that the American media’s coverage of soccer is lopsided, and that they do play a role in the sport’s difficulty to become popular in the American mainstream.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.