Nurul Athirah Nasarudin, Fatma Al-Jasmi, Nor Hidayati Abdul Aziz, Nor Azlina Ab Aziz, Wasif Khan, Yusuf Hendrawan, Dimas Firmanda Al Riza, Ayisha Manzoor, Bassam R. Ali, Mohd Saberi Mohamad
Background: Breast cancer is a leading cause of mortality among women worldwide. Accurate survival prediction can improve clinical decision-making and support personalized treatment planning. This study aims to develop an interpretable and effective deep learning model for breast cancer survival prediction using multi-omics data. Methods: This study proposes a novel deep learning model combining Bi-directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures, integrated with Minimum Redundancy Maximum Relevance (MRMR) feature selection. The model was evaluated on two large datasets: METABRIC (n=1980) and TCGA-BRCA (n=1080), using clinical, copy number alteration (CNA), and gene expression data. Performance was assessed through metrics such as AUC-ROC and accuracy. Results: The proposed model demonstrated superior performance compared to existing algorithms, achieving high AUC-ROC and accuracy values across all data modalities. The integration of BiLSTM and CNN architectures allowed the model to capture temporal and spatial patterns, improving prediction robustness. Notably, the model achieved an accuracy of 98% on the METABRIC dataset and 96% on the TCGA dataset. Conclusions: The combination of BiLSTM, CNN, and MRMR offers an interpretable and accurate framework for breast cancer survival prediction using multi-omics data. This approach provides actionable insights for clinicians and highlights its potential for broader applications in oncology. Copyright: © 2026 Nasarudin NA et al.
Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Abu Dhabi, Al Ain, 17666, United Arab Emirates; Centre for Advanced Analytics, CoE for Artificial Intelligence, Multimedia University, Malacca, Malacca, 75450, Malaysia; Faculty of Engineering & Technology, Multimedia University, Malacca, Malacca, 75450, Malaysia; J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, 32 611, FL, United States; Department of Biosystems Engineering, Faculty of Agricultural Technology, Universitas Brawijaya, East Java, Malang, 65145, Indonesia
