Presentation
Machine Learning Applications for Early-Stage Ovarian Cancer Diagnosis
DescriptionOvarian cancer (OC) significantly impacts women's health, and despite its prevalence, remains without a definitive cure. Early detection is crucial for improving treatment outcomes and reducing mortality rates and healthcare system costs. Leveraging advancements in machine learning, our study seeks to empower physicians with tools for more confident and timely diagnosis. This study introduces a novel approach using machine learning to enhance early-stage OC diagnosis. We propose the Data Driven Diagnosis Framework (DDD), a new feature extraction and ensemble method that improves classification accuracy. Using models such as Random Forest, Logistic Regression, Decision Tree, Gradient Boosting Machine, Extreme Gradient Boosting Machine, and language models, our approach shows accuracy improvements of up to 14%-28% over state-of-the-art methods.

Event Type
ACM Student Research Competition: Undergraduate Poster
Posters
TimeWednesday, 20 November 20242:15pm - 2:30pm EST
LocationB306
TP