Federated Explainable Artificial Intelligence for Early Diabetes Prediction Across Multi-Hospital Data

Abstract

In healthcare system, detection of disease earlier helps in patient health management. The same is applicable to diabetes mellitus management and treatment. Centralized data is where conventional machine depends on, but the centralized data has issues related to privacy and security in the healthcare system. Federated Explainable Artificial Intelligence (FedXAI) framework in early diabetes prediction across multiple hospital settings is the focus of this work. This work, proposed using federated learning to collaboratively train model, with restrictions to sensitive patient data. The integration of SHapley Additive explanations (SHAP) to the system is for transparency enhancement and clinical trust. SHAP integration is insightful in the provision of interpretability in the model predictions. The Pima Indians Diabetes dataset helps stimulate distributed hospital settings for independent and non-independent (nonIID) data distribution. The outcome of the work indicated federated model is of higher competitiveness in comparison to the centralized approach. There is a decrease in accuracy with heterogeneous distributions. The glucose level, body mass index (BMI), and age remains the major predictor of diabetes as regards to the explainability analysis. Practically, the proposed system enhances privacy protection and preservation, solves interpretable issues in application for real- world healthcare deployments.