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Using Machine Learning to Predict Breast Cancer with Hormonal Data

AJAS · 2025 Biomedical and Health Sciences (inferred)

Joshua Kim · Tennessee Academy of Science

Overview

Mammograms are currently the primary method used for breast cancer detection. Although they have become much more commonplace in the past few decades, they still remain shockingly inaccurate, often yielding false negative results (~12.5%). As per data from the American Cancer Society, mammograms miss one out of eight breast cancers. Additionally, there exist prolonged delays in the breast cancer diagnostic process, with a median of 61 days from a patient’s first symptoms to the final steps of diagnosis. These factors allow for lower-stage cancerous tumors to grow and metastasize, or spread throughout the body. Most breast cancer patients are female but knowledge about the influence of female-specific hormones, i.e., estrogen and progesterone, on hormone receptor-positive breast cancer is currently imperfect and conflicting.

Competition history

  • AJAS 2025 Category not listed

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Source: AAAS Annual Meeting (Confex) / American Junior Academy of Science

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