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 Conference Paper Faculty Publications- Conference Papers 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 Debnath, Jewell; George, Jossy Title A name given to the resource Non-Invasive Detection of Ovarian Cancer using Biosensors Framework with Machine Learning and Federated Learning Techniques 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 Proceedings of the 6th International Conference on Smart Electronics and Communication, ICOSEC 2025;pp.529-534 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICOSEC67334.2025.11459750" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICOSEC67334.2025.11459750</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037205914?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037205914?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 Debnath J., Christ (Deemed To Be University), India; George J., Christ (Deemed To Be University), India Description An account of the resource Ovarian cancer is a leading cause of death worldwide, frequently diagnosed at advanced stages due to the lack of effective early screening methods. This work proposes a non-invasive cancer diagnostics utilizing amperometric electrochemical biosensors in early cancer detection from biological fluids, such as urine-based by combination of specific biomarkers like HE4 and Ca125, which are closely associated with ovarian cancer. This study approach integrates machine learning models to work with biosensor data for cancer classification tasks, and federated learning methods to ensure patient data privacy. The proposed system achieves diagnostic results using a synthetic dataset with over 98% accuracy. This decentralized healthcare solution demonstrates early ovarian cancer detection and improved patient outcomes by combining predictive capability with privacy preservation. 2025 IEEE. Subject The topic of the resource Biosensor; Federated Learning; Machine Learning; Non-invasive method; Ovarian cancer Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159859-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online