Multi-modal Stacked Ensemble Model for Breast Cancer Prognosis Prediction
Keywords:
Breast cancer prognosis prediction, optimized CNN, Tug of War algorithm, stacked-ensemble learning
Abstract
Breast cancer (BC) is a global health challenge that affects millions of women worldwide and leads to significant mortality. Recent advancements in next-generation sequencing technology have enabled comprehensive diagnosis and prognosis determination using multiple data modalities. Deep learning methods have shown promise in utilizing these multimodal data sources, outperforming single-modal models. However, integrating these heterogeneous data sources poses significant challenges in clinical decision-making. This study proposes an optimized multimodal CNN for a stacked ensemble model (OMCNNSE) for breast cancer prognosis. Our novel method involves the integration of the Tug of War (TWO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN), enhancing feature extraction from three distinct multimodal datasets: clinical profile data, copy number alteration (CNA), and gene expression data. Specifically, we employ the TWO algorithm to optimize separate CNN models for each dataset, identifying optimal values for the hyperparameters. We then trained the three baseline CNN models using the optimized values through 10-fold crossvalidation. Finally, we utilize an ensemble learning approach to integrate the models’ predictions and apply an SVM classifier for the final prediction. To evaluate the proposed method, we conducted experiments on the METABRIC breast cancer dataset comprising diverse patient profiles. Our results demonstrated the effectiveness of the OMCNNSE approach for predicting breast cancer prognosis. The model achieved high AUC, accuracy, sensitivity, precision, and MCC, outperforming traditional single-modal models and other state-of-the-art methods.
Published
2024-10-15
How to Cite
Maigari, A., Zainol, Z., & Xinying, C. (2024). Multi-modal Stacked Ensemble Model for Breast Cancer Prognosis Prediction. Statistics, Optimization & Information Computing, 13(3), 1013-1034. https://doi.org/10.19139/soic-2310-5070-2100
Issue
Section
Research Articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).