Journal Article

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

Charizanos, Georgios, Demirhan, Haydar, İçen, Duygu

2024

Overview

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 classification performance due to fuzzy logic’s better handling of imbalance and separation issues. Hence, classification accuracy is improved, mitigating the risk of misclassified conditions and biased insights for clinical study patients. Results The performance of the fuzzy logistic regression model is assessed on twelve binary classification problems with clinical datasets. The model has consistently high sensitivity, specificity, F1, precision, and Mathew’s correlation coefficient scores across all clinical datasets. There is no evidence of impact from the imbalance or separation that exists in the datasets. Furthermore, we compare the fuzzy logistic regression classification performance against two versions of classical logistic regression and six different benchmark sources in the literature. These six sources provide a total of ten different proposed methodologies, and the comparison occurs by calculating the same set of classification performance scores for each method. Either imbalance or separation impacts seven out of ten methodologies. The remaining three produce better classification performance in their respective clinical studies. However, these are all outperformed by the fuzzy logistic regression framework. Conclusion Fuzzy logistic regression showcases strong performance against imbalance and separation, providing accurate predictions and, hence, informative insights for classifying patients in clinical studies.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Binary classification / Binary system (Mathematics) / Breast cancer / Class imbalance / Classification / Clinical studies / Datasets / Decision trees / Fuzzification / Fuzzy algorithms / Fuzzy Logic / Fuzzy logistic regression / Fuzzy systems / Health Sciences / Humans / Logistic Models / Logistic regression / Machine learning / Medical diagnosis / Medical research / Medicine / Medicine & Public Health / Medicine, Experimental / Methods / Older people / Patients / Regression analysis / Research methodology / Separation / Statistical Theory and Methods / Statistics for Life Sciences / Support vector machines / Theory of Medicine/Bioethics