Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies.

Journal: BMC medical research methodology
Published Date: Jul 5, 2024

Abstract

BACKGROUND: In binary classification for clinical studies, an imbalanced distribution of cases to classes and an extreme association level between the binary dependent variable and a subset of independent variables can create significant classification problems. These crucial issues, namely class imbalance and complete separation, lead to classification inaccuracy and biased results in clinical studies.

Authors

  • Georgios Charizanos
    Mathematical Sciences, School of Science, RMIT University, La Trobe St, Melbourne, 3000, Victoria, Australia.
  • Haydar Demirhan
    Mathematical Sciences, School of Science, RMIT University, La Trobe St, Melbourne, 3000, Victoria, Australia. haydar.demirhan@rmit.edu.au.
  • Duygu İçen
    Department of Statistics, Hacettepe University, Çankaya, Ankara, 06800, Ankara, Türkiye.

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