Federated, Explainable and Imbalance-Aware Framework for Early Disease-Risk Prediction
DOI:
https://doi.org/10.5281/ijurd.v1i4.9Keywords:
early disease prediction, explainable AI, federated learning, class imbalance, multi-modal fusionAbstract
Early detection of chronic non-communicable diseases can reduce mortality and cost. We present an end-to-end pipeline that (1) fuses longitudinal electronic health records, laboratory results and behavioural variables via attention-based multi-modal learning, (2) trains in a federated, privacy-preserving manner with differential privacy guarantees, (3) handles severe class-imbalance through cost-sensitive learning and Borderline-SMOTE, and (4) delivers clinician-oriented explanations using SHAP values. Experiments on two Indian hospital cohorts (29 733 patients) show an absolute gain of +6.8% AUROC and +7.4% sensitivity over the best centralised baseline while maintaining 90% specificity. The system generalises across institutions and is suitable for resource-constrained settings.References
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Copyright (c) 2025 Aayush Chaudhary
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