An ensembled framework for human breast cancer survivability prediction using deep learning
Open accessAbstract
Breast cancer is categorized as an aggressive disease, and it is one of the leading causes of death. Accurate survival predictions for both long-term and short-term survivors, when delivered on time, can help physicians make effective treatment decisions for their patients. Therefore, there is a dire need to design an efficient and rapid computational model for breast cancer prognosis. In this study, we propose an ensemble model for breast cancer survivability prediction (EBCSP) that utilizes multi-modal data and stacks the output of multiple neural networks. Specifically, we design a convolutional neural network (CNN) for clinical modalities, a deep neural network (DNN) for copy number variations (CNV), and a long short-term memory (LSTM) architecture for gene expression modalities to effectively handle multi-dimensional data. The independent models' results are then used for binary classification (long term > 5 years and short term < 5 years) based on survivability using the random forest method. The EBCSP model's successful application outperforms models that utilize a single data modality for prediction and existing benchmarks.
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- Authors
- Mustafa, E., Jadoon, E. K., Khaliq-uz-Zaman, S., Humayun, M. A., & Maray, M.
- Affiliations
- COMSATS University Islamabad (PK)
- Department of Computer Science, Comsats University Islamabad, Abbottabad Campus, Islamabad 22060, Pakistan
- Information Technology University (PK)
- King Khalid University (SA)
- Corresponding author
- Mustafa, Ehzaz
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2023-05-10
- Volume
- 13
- Issue
- 10
- Pages
- 1688
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC10217686
- OpenAlex
- W4378515984
- Cited by
- 24
- References
- 36
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 6 · 2023: 3
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- the Deanship of Scientific Research at King Khalid University (235/1444)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- breast cancer, survival prediction, prognosis, ensemble learning, multimodal fusion, clinical data, copy number variation, gene expression, convolutional neural networks, long short-term memory, random forest, binary classification
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