Fairness in AI systems is critical in high-stakes domains such as healthcare, where biased predictions can exacerbate existing disparities. This paper presents an empirical evaluation of a three-stage fairness pipeline, integrating pre-processing (Disparate Impact Remover), in-processing (Exponentiated Gradient Reduction), and post-processing (Equalized Odds Optimization), on a real-world healthcare dataset from Ireland. We construct an intersectional demographic attribute to audit disparities across race, gender, and age. Our results show that multi-stage fairness interventions can reduce subgroup disparities with minimal loss in predictive performance. However, integrating fairness techniques may introduce fairness and performance trade-offs. These findings highlight the importance of holistic, intersectional fairness auditing and the need for careful design of fairnessenhancing pipelines in real-world applications.