Purpose: This qualitative research aimed to create a developmentally and gender-appropriate game-based intervention to promote Human Papillomavirus (HPV) vaccination in adolescents. <br/>Background: Ranking as the most common sexually transmitted infection, about 80 million Americans are currently infected by HPV, and it continues to increase with an estimated 14 million new cases yearly. Certain types of HPV have been significantly associated with cervical, vaginal, and vulvar cancers in women; penile cancers in men; and oropharyngeal and anal cancers in both men and women. Despite HPV vaccination being one of the most effective methods in preventing HPV-associated cancers, vaccination rates remain suboptimal in adolescents. Game-based intervention, a novel medium that is popular with adolescents, has been shown to be effective in promoting health behaviors. <br/>Methods: Sample/Sampling. We used purposeful sampling to recruit eight adolescent-parent dyads (N = 16) which represented both sexes (4 boys, 4 girls) and different racial/ethnic groups (White, Black, Latino, Asian American) in the United States. The inclusion criteria for the dyads were: (1) a child aged 11-14 years and his/her parent, and (2) ability to speak, read, write, and understand English. Procedure. After eligible families consented to their participation, semi-structured interviews (each 60-90 minutes long) were conducted with each adolescent-parent dyad in a quiet and private room. Each dyad received $50 to acknowledge their time and effort. Measure. The interview questions consisted of two parts: (a) those related to game design, functioning, and feasibility of implementation; (b) those related to theoretical constructs of the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB). Data analysis. The interviews were audio-recorded with permission and manually transcribed into textual data. Two researchers confirmed the verbatim transcription. We use pre-developed codes to identify each participant’s responses and organize data and develop themes based on the HBM and TPB constructs. After the analysis was completed, three researchers in the team reviewed the results and discussed the discrepancies until a consensus is reached.<br/>Results: The findings suggested that the most common motivating factors for adolescents’ HPV vaccination were its effectiveness, benefits, convenience, affordable cost, reminders via text, and recommendation by a health care provider. Regarding the content included in the HPV game, participants suggested including information about who and when should receive the vaccine, what is HPV and the vaccination, what are the consequences if infected, the side effects of the vaccine, and where to receive the vaccine. The preferred game design elements were: 15 minutes long, stories about fighting or action, option to choose characters/avatars, motivating factors (i.e., rewards such as allowing users to advance levels and receive coins when correctly answering questions), use of a portable electronic device (e.g., tablet) to deliver the education. Participants were open to multiplayer function which assists in a facilitated conversation about HPV and the HPV vaccine. Overall, the participants concluded enthusiasm for an interactive yet engaging game-based intervention to learn about the HPV vaccine with the goal to increase HPV vaccination in adolescents. <br/>Implications: Tailored educational games have the potential to decrease the stigma of HPV and HPV vaccination, increasing communication between the adolescent, parent, and healthcare provider, as well as increase the overall HPV vaccination rate.

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.