Enhancing Breast Cancer Prediction with LLM-Inferred Confounders

Wheeler High School, Marietta, GA Abstract This study enhances breast cancer prediction by using large language models to infer the likelihood of confounding diseases, namely diabetes, obesity, and cardiovascular disease, from routine clinical data. These AI-generated features improved Random Forest model performance, particularly for LLMs like Gemma (3.9%) and Llama (6.4%). The approach shows promise for noninvasive prescreening and clinical integration, supporting improved early detection and shared decision-making in breast cancer diagnosis. Introduction Breast cancer (BC) is a leading cause of death among women in the U.S., with most cases having unknown causes despite known risk factors1. Researchers have identified correlations between BC and various clinical features and biomarkers, such as body mass index, glucose, insulin, leptin, adiponectin, resistin, MCP-1, and HOMA, that can be measured through routine blood tests.