Optimizing Breast Cancer Prediction: A Multimodal Dataset Apporach with XGBOOST

This study presents a novel approach to enhance the accuracy of breast cancer detection from mammogram images through a hybrid feature selection and classification framework. Leveraging the power of XGBoost, a state-of-the-art machine learning algorithm, an embedded genetic algorithm is introduced for optimal feature selection. The genetic algorithm refines the feature set by iteratively evolving towards a subset that maximizes the discriminative power for breast cancer diagnosis. Subsequently, the selected features are fed into a Recurrent Neural Network (RNN) architecture with Random Boolean Networks (RBN) for classification. The RNN-RBN model captures intricate temporal dependencies within the image data, providing a nuanced understanding of the complex patterns indicative of breast cancer. The synergistic coupling of the XGBoost-embedded genetic algorithm for feature selection and the RNN-RBN model for classification results in a robust and interpretable system for breast cancer detection. The proposed hybrid approach is evaluated on a comprehensive dataset of mammogram images, demonstrating superior performance compared to traditional methods. The combination of feature selection through XGBoost- embedded genetic algorithms and RNN-RBN classification showcases the potential for advanced, accurate, and efficient breast cancer diagnosis, holding promise for improving early detection rates and patient outcomes in clinical settings.