Citation

Nasarudin, Nurul Athirah and Al-Jasmi, Fatma and Abdul Aziz, Nor Hidayati and Ab Aziz, Nor Azlina and Khan, Wasif and Hendrawan, Yusuf and Al Riza, Dimas Firmanda and Manzoor, Ayisha and Ali, Bassam R. and Mohamad, Mohd Saberi (2026) An Improved Deep Learning Algorithm for Breast Cancer Survival Prediction Based on Multi-Omics Data. F1000Research, 14. p. 765. ISSN 2046-1402

Text 417efb46-8a7b-4784-b42b-435f50622ec4_f1000res166682.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence, BiLSTM, Breast Cancer, CNN, Deep Learning, Multi-omics
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Jun 2026 06:24
Last Modified:	04 Jun 2026 06:24
URII:	http://shdl.mmu.edu.my/id/eprint/15934

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