Non-Invasive Detection of Ovarian Cancer using Biosensors Framework with Machine Learning and Federated Learning Techniques
- Title
- Non-Invasive Detection of Ovarian Cancer using Biosensors Framework with Machine Learning and Federated Learning Techniques
- Creator
- Debnath, Jewell; George, Jossy
- Description
- 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.
- Source
- Proceedings of the 6th International Conference on Smart Electronics and Communication, ICOSEC 2025;pp.529-534
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Biosensor; Federated Learning; Machine Learning; Non-invasive method; Ovarian cancer
- Coverage
- Debnath J., Christ (Deemed To Be University), India; George J., Christ (Deemed To Be University), India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833159859-4;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Debnath, Jewell; George, Jossy, “Non-Invasive Detection of Ovarian Cancer using Biosensors Framework with Machine Learning and Federated Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26081.