Firefly Optimized Federated SVM Model for Breast Cancer Prediction
Y. Supriya, Chemmalar Selvi G, Gokul Yenduri, Gautam Srivastava, Thippa Reddy Gadekallu · 2023
Cancer is a leading cause of morbidity and mortality worldwide, with an estimation of 10 million deaths attributed to cancer each year. Among various types of cancer, Breast cancer is the most common cancer diagnosed among women, accounting for approximately 30% of all new cancer cases among women. Periodic clinical checks and self-tests will assist in the early identification of Breast cancer. Breast cancer detection at an early stage helps the patients to receive suitable treatment, which can increase the chances of their survivability. Automated clinical solutions, such as Computer-Aided detection and AI-based algorithms, have shown increasing promise in improving the accuracy and efficiency of Breast cancer screening and diagnosis. Several serious challenges have to be addressed such as lack of standardization and regulation of these technologies, the need for large amounts of data for training and validation, as well as patients’ privacy and data security. To address these challenges, a novel Federated Learning architecture that, rather than sharing data, enables knowledge integration by sharing the model parameters of each client during the federated training process. In this paper, we proposed a federated Support Vector Machine for the early detection of Breast cancer. This work utilized the potential benefits of Federated Learning, Firefly algorithm, and Support Vector Machine to study the early detection of Breast cancer disease. The proposed model leverages a distributed computing approach, allowing the SVM model to be trained across multiple clients while preserving data privacy. In addition, we used the Firefly algorithm as a feature selection technique to identify the most relevant features for Breast cancer detection. We evaluated the performance of the proposed model using publicly available Breast cancer datasets namely Wisconsin dataset. The experimental results of this proposed work showed that our proposed model has achieved an accuracy of 95.68%. Finally, we highlighted the significance of the proposed work with the potential benefits of employing Federated Learning in Breast cancer detection.