Impact of Tumor Location on Predicting Early-Stage Breast Cancer Patient Survivability Using Explainable Machine Learning Models.

Journal: JCO clinical cancer informatics
PMID: 40163811

Abstract

PURPOSE: This study aims to investigate the impact of tumor quadrant location on the 5-year early-stage breast cancer survivability prediction using explainable machine learning (ML) models. By integrating these predictive models with Shapley Additive Explanations (SHAP), feature importance, and coefficient effect size, we aim to provide insights into the significant factors influencing patient outcomes.

Authors

  • Nader Abdalnabi
    MU Institute for Data Science and Informatics, Columbia, MO.
  • Abdulmateen Adebiyi
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO.
  • Ahmad Alhonainy
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO.
  • Kushal Naha
    Department of Medicine, University of Missouri, Columbia, MO.
  • Christos Papageorgiou
    Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA.
  • Praveen Rao
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.

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