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<p>This research paper investigates the impact of demographic biases, specifically race and gender, on machine learning-based glioma grading, using the TCGA data set compiled from The Cancer Genome Atlas. The study applies three common classifiers (logistic regression, random forests, and extreme gradient boosting) and explores pre-processing (reweighting) and post-processing (equalized odds) strategies for bias mitigation. It evaluates prediction performance metrics (Matthews correlation coefficient, recall, specificity) and fairness metrics (disparate impact, equal opportunity difference, error rate difference), highlighting the trade-offs between fairness and accuracy across different demographic groups. For the most severe bias (race), the pre-trained logistic regression model using the reweighting algorithm shows some deterioration in prediction outcomes for the under-represented group and even an increase in unfairness, while the post-processing approach improves results for the under-represented group and provides significant improvements in fairness. These results are interesting because they could be taken into account in real-world clinical decision-making or in outcomes for under-represented patient groups.</p>

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Addressing the balance between fairness and performance in glioma grade prediction using bias mitigation techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月18日

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  • Raquel Sánchez-Marqués,
  • Vicente García,
  • J. Salvador Sánchez

摘要

This research paper investigates the impact of demographic biases, specifically race and gender, on machine learning-based glioma grading, using the TCGA data set compiled from The Cancer Genome Atlas. The study applies three common classifiers (logistic regression, random forests, and extreme gradient boosting) and explores pre-processing (reweighting) and post-processing (equalized odds) strategies for bias mitigation. It evaluates prediction performance metrics (Matthews correlation coefficient, recall, specificity) and fairness metrics (disparate impact, equal opportunity difference, error rate difference), highlighting the trade-offs between fairness and accuracy across different demographic groups. For the most severe bias (race), the pre-trained logistic regression model using the reweighting algorithm shows some deterioration in prediction outcomes for the under-represented group and even an increase in unfairness, while the post-processing approach improves results for the under-represented group and provides significant improvements in fairness. These results are interesting because they could be taken into account in real-world clinical decision-making or in outcomes for under-represented patient groups.