Federated and Explainable AI Models for Privacy-Preserving Predictive Analytics in Healthcare System

Healthcare systems increasingly rely on predictive analytics to improve patient outcomes, yet concerns about data privacy and model transparency remain significant barriers to adoption. This paper presents a novel framework integrating Federated Learning (FL) with Explainable Artificial Intelligence (XAI) to enable privacy-preserving predictive analytics in distributed healthcare environments. Our approach allows multiple healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data, addressing critical privacy regulations such as HIPAA and GDPR. We incorporate explainability techniques including SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide clinically interpretable insights into model predictions. The framework is evaluated on real-world healthcare datasets for disease prediction and risk stratification tasks. Results demonstrate that federated models achieve comparable accuracy to centralized approaches while maintaining data privacy, with explainability layers enhancing clinical trust and adoption. Our work bridges the gap between advanced predictive analytics and practical healthcare deployment, offering a scalable solution that balances model performance, privacy preservation, and interpretability. This research contributes to the advancement of trustworthy AI in healthcare systems.