Equitable Diabetes Diagnosis: Tackling Ethnic and Gender Disparities - Ruback, Lívia; Felix, Luisa; Soares Teles, Ariel (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais do XL Simpósio Brasileiro de Bancos de Dados, 2025)
Machine Learning (ML) has advanced disease diagnosis in healthcare, but raises fairness concerns, as model biases can perpetuate social inequalities. This study aims to evaluate and mitigate bias in diabetes diagnosis prediction models. We conducted experiments considering ethnicity and gender as protected attributes, evaluating bias using the fairness metrics Statistical Parity Difference, Equal Opportunity Difference, and Average Odds Difference. We applied the bias mitigation techniques Reweighing and Prejudice Remover, which showed improvements in fairness metrics, with a reduction in disparities between groups, while maintaining model accuracy. These findings reinforce the need to integrate fairness considerations into ML models for healthcare applications.