Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Namitha, T.N.; Raghavendra, S.; Vinith, R. Title A name given to the resource Privacy-Preserving Federated Learning for Prognostic Modeling in Rare Diseases: A Scalable Case Study on Kawasaki Disease Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Online and Biomedical Engineering;Volume;21;Issue;11;pp.66-80 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3991/ijoe.v21i11.56385" target="_blank" rel="noreferrer noopener">https://doi.org/10.3991/ijoe.v21i11.56385</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017085885?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017085885?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Namitha T.N., Department of CSE, Christ (Deemed to be University), Karnataka, Bangalore, India; Raghavendra S., Department of AIML and Data Science, Christ (Deemed to be University), Karnataka, Bangalore, India; Vinith R., Amrita Vishwa Vidyapeetham, Tamil Nadu, Coimbatore, India Description An account of the resource Predictive modeling in rare diseases faces major challenges, including data scarcity, class imbalance, and strict privacy regulations that limit cross-border collaboration. These challenges are particularly critical in Kawasaki disease (KD)a rare vasculitis in childrenwhere 10% to 20% of patients are resistant to intravenous immunoglobulin (IVIG), the standard first-line treatment. This significantly increases the risk of coronary artery abnormalities (CAA), making early and accurate prediction of resistance to IVIG essential for improving patient outcomes. Our work proposes a federated learning (FL) approach to address the constraints imposed by security and privacy concerns. We investigate convolutional neural networks (CNN) as the shared model, collaboratively trained across clients. Coupled with strategies to address class imbalance resulting from the rarity of the condition, the federated approach yielded promising results when evaluated against conventional machine learning (ML) models. The proposed approach demonstrated strong performance, achieving 94% accuracy, 93% precision, 89% recall, and 91% F1 score. To ensure robustness and generalizability, an independent dataset was also used, where the proposed model excelled similarly. These results highlight the potential of FL to overcome data privacy barriers and provide a scalable, secure solution for predictive modeling in rare diseases, supporting its integration into medical prediction workflows. 2025 by the authors of this article. Subject The topic of the resource adaptive synthetic sampling; convolutional neural network (CNN); federated learning (FL); flower framework; intravenous immunoglobulin resistance; Kawasaki disease (KD); rare disease Publisher An entity responsible for making the resource available International Federation of Engineering Education Societies (IFEES) Relation A related resource ISSN: 26268493; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online