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

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.

Method

To deal with class imbalance and complete separation problems, we propose using a fuzzy logistic regression framework for binary classification. Fuzzy logistic regression incorporates combinations of triangular fuzzy numbers for the coefficients, inputs, and outputs and produces crisp classification results. The fuzzy logistic regression framework shows strong classific

SUBMITTER: Charizanos G 

PROVIDER: S-EPMC11225249 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature