Predicting cancer survival at different stages: Insights from fair and explainable machine learning approaches.

Journal: International journal of medical informatics
PMID: 39970491

Abstract

OBJECTIVES: While prior machine learning (ML) models for cancer survivability prediction often treated all cancer stages uniformly, cancer survivability prediction should involve understanding how different stages impact the outcomes. Additionally, the success of ML-powered cancer survival prediction models depends a lot on being fair and easy to understand, especially for different stages of cancer. This study addresses cancer survivability prediction using fair and explainable ML methods.

Authors

  • Tejasvi Sanjay Kamble
    School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
  • Hongtao Wang
  • Nicole Myers
    School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, USA.
  • Nickolas Littlefield
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Leah Reid
    Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA 15260, United States.
  • Cynthia S McCarthy
    School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
  • Young Ji Lee
    Department of Health and Community Systems, School of Nursing, University of Pittsburgh, Pittsburgh, PA, United States.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Liron Pantanowitz
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Soheyla Amirian
    School of Computing, University of Georgia, Athens, GA, 30602 USA.
  • Hooman H Rashidi
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Electronic address: rashidihh@upmc.edu.
  • Ahmad P Tafti
    School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA.

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