Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study.

Journal: Frontiers in oncology
Published Date: Feb 15, 2024

Abstract

INTRODUCTION: This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors.

Authors

  • Mostafa Dianati-Nasab
    School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia.
  • Khodakaram Salimifard
    Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Bushehr, Iran.
  • Reza Mohammadi
    Department of Operation Management, Amsterdam Business School, University of Amsterdam, Amsterdam, Netherlands.
  • Sara Saadatmand
    Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Bushehr, Iran.
  • Mohammad Fararouei
    School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia.
  • Kosar S Hosseini
    Department of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Behshid Jiavid-Sharifi
    School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia.
  • Thierry Chaussalet
    Computer Science and Engineering, University of Westminster, London, United Kingdom.
  • Samira Dehdar
    Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Bushehr, Iran.

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