Paper Type
Complete
Abstract
This study investigates whether data balancing techniques improve fairness across gender groups in stroke prediction using Logistic Regression on a highly imbalanced Kaggle stroke dataset. Six resampling methods were evaluated for predictive performance and gender fairness. Results show that resampling significantly enhances minority class detection, but resampling methods introduce significant gender disparities, indicating that data balancing does not inherently ensure fairness. These findings underscore the need for fairness-aware strategies in healthcare AI to mitigate biases while improving predictive accuracy.
Paper Number
1585
Recommended Citation
Tourt, Dara; Rebman, Carl Jr.; Booker, Queen; and Levkoff, Steve, "Does Data Balancing Improve Fairness Across Gender Groups? A Comparative Study of Highly Imbalanced Stroke Prediction with a Logistic Regression Model" (2026). AMCIS 2026 Proceedings. 17.
https://aisel.aisnet.org/amcis2026/sig_dsa/sig_dsa/17
Does Data Balancing Improve Fairness Across Gender Groups? A Comparative Study of Highly Imbalanced Stroke Prediction with a Logistic Regression Model
This study investigates whether data balancing techniques improve fairness across gender groups in stroke prediction using Logistic Regression on a highly imbalanced Kaggle stroke dataset. Six resampling methods were evaluated for predictive performance and gender fairness. Results show that resampling significantly enhances minority class detection, but resampling methods introduce significant gender disparities, indicating that data balancing does not inherently ensure fairness. These findings underscore the need for fairness-aware strategies in healthcare AI to mitigate biases while improving predictive accuracy.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIG DSA