Easy ensemble classifier-group and intersectional fairness and threshold (EEC-GIFT): a fairness-aware machine learning framework for lung cancer screening eligibility using real-world data.

Abstract

Authors

• Piyawan Conahan
  Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
• Lary A Robinson
  Division of Thoracic Oncology (Surgery), H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
• Trung Le
  Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, United States.
• Gilmer Valdes
  Department of Radiation Oncology, University of California, San Francisco, California.
• Matthew B Schabath
  Associate Member, Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
• Margaret M Byrne
  Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
• Lee Green
  Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
• Issam El Naqa
  Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.
• Yi Luo
  Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.

Keywords

External Resources