Date of Award

January 2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Indigenous Health

First Advisor

Shawnda Schroeder

Abstract

Colorectal cancer (CRC) remains a leading cause of morbidity and mortality among Indigenous and Pacific Islander populations, particularly in American Samoa, where screening rates are critically low due to cultural barriers, limited healthcare infrastructure, and geographical isolation. This dissertation explores the potential of an Artificial Intelligence and Machine Learning (AI/ML) predictive model to enhance CRC screening and early detection among American Samoan adults. By leveraging widely available clinical data, such as complete blood count (CBC) results, age, and gender, the AI/ML model aims to identify high-risk individuals for targeted interventions, thereby improving early detection and treatment outcomes.The dissertation comprises three interrelated products: a manuscript, an evaluation plan, and a grant application. The manuscript presents findings from a pilot study that tested the AI/ML model’s ability to predict CRC risk, assess its validity, and explore its feasibility in a resource-limited setting. The evaluation plan systematically examines the implementation process, integrating both Indigenous and Westernized frameworks to ensure methodological rigor, cultural appropriateness, and community engagement. The grant application seeks funding to expand this research, allowing for the integration of additional biomarkers, validation with larger cohorts, and potential deployment in other Pacific Islander communities facing similar healthcare disparities. This research is significant as it introduces a non-invasive, data-driven approach to CRC screening that aligns with cultural sensitivities while addressing systemic barriers to healthcare access. The AI/ML model offers a cost-effective alternative to traditional screening methods, potentially transforming CRC prevention strategies in American Samoa and beyond. The integration of Indigenous knowledge with cutting-edge technology underscores the importance of culturally grounded innovation in health equity research. Ultimately, this dissertation contributes to the advancement of precision medicine for underrepresented populations, providing a scalable solution to mitigate health disparities in CRC screening and early detection.

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