Abstract
In this study, we introduce a machine learning optimized graphene-based biosensor tailored for the early and accurate detection of breast cancer, aiming to elevate diagnostic reliability and clinical efficacy. The device employs a multilayer Ag-SiO₂-Ag architecture to amplify optical response, achieving a peak sensitivity of 1785 nm/RIU. Machine learning models are used to optimize structural parameters, enabling systematic refinement of detection accuracy and reproducibility. The optimized design demonstrates superior sensitivity compared with conventional biosensor configurations, underscoring its effectiveness in bioanalytical applications. The proposed platform offers a precise and robust solution for breast cancer screening and monitoring, with strong potential for clinical translation. To further refine sensor efficacy, a comprehensive parametric optimization approach is employed, strategically enhancing its sensitivity metrics. The sensor's heightened precision and responsiveness position it as a promising tool in biomedical diagnostics, particularly for early-stage breast cancer screening and monitoring.