Analyzing the Trade-off Between Fairness and Model Performance in Supervised Learning: A Case Study in the MIMIC dataset - Silva, Bruno Pires M.; Berton, Lilian (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2025: Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, 2025)
Fairness has become a key area in machine learning (ML), aiming to ensure equitable outcomes across demographic groups and mitigate biases. This study examines fairness in healthcare using the MIMIC III dataset, comparing traditional and fair ML approaches in pre, in, and post-processing stages. Methods include Correlation Remover and Adversarial Learning from Fairlearn, and Equalized Odds Post-processing from AI Fairness 360. We evaluate performance (accuracy, F1-score) alongside fairness metrics (equal opportunity, equalized odds) considering different sensible attributes. Notably, Equalized Odds Post-processing improved fairness with less performance loss, highlighting the trade-off between fairness and predictive accuracy in healthcare models.