Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma
The research method applies robust feature selection approaches to ultra-high-dimensional survival data records from Renal Cell Carcinoma patients through deep learning methodologies. The linear methods LASSO and Elastic Net encounter failure when processing data because they face simultaneous mult...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nigerian Society of Physical Sciences
2025-11-01
|
| Series: | Journal of Nigerian Society of Physical Sciences |
| Subjects: | |
| Online Access: | https://journal.nsps.org.ng/index.php/jnsps/article/view/2772 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849317449970745344 |
|---|---|
| author | Shaymaa Mohammed Ahmed Majid Khan Majahar Ali Raja Aqib Shamim |
| author_facet | Shaymaa Mohammed Ahmed Majid Khan Majahar Ali Raja Aqib Shamim |
| author_sort | Shaymaa Mohammed Ahmed |
| collection | DOAJ |
| description |
The research method applies robust feature selection approaches to ultra-high-dimensional survival data records from Renal Cell Carcinoma patients through deep learning methodologies. The linear methods LASSO and Elastic Net encounter failure when processing data because they face simultaneous multicollinearity issues in addition to overfitting effects and produce marginal survival outcome variability prediction at 54%. We suggest combining ISIS with deep learning architectures featuring PCA-RFA-RSIS models as a remedy to handle these present limitations. Among all evaluated methods PCA-RFA-RSIS is proved most accurate with an MSE measurement of 24.39 and R2 value of 0.89. PCA improved the model’s dimensionality reduction power and robust ISIS maintained model stability despite outliers present in the data. The discovery holds significant value in precision medicine because it creates opportunities to develop individualized therapy for kidney failure patients. Further research needs to enhance hybrid models and expand their utilization between different diseases as well as complex biological systems.
|
| format | Article |
| id | doaj-art-6cb33261d6344417abc21b74cfbd77c0 |
| institution | Kabale University |
| issn | 2714-2817 2714-4704 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | Journal of Nigerian Society of Physical Sciences |
| spelling | doaj-art-6cb33261d6344417abc21b74cfbd77c02025-08-20T03:51:13ZengNigerian Society of Physical SciencesJournal of Nigerian Society of Physical Sciences2714-28172714-47042025-11-017410.46481/jnsps.2025.2772Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinomaShaymaa Mohammed AhmedMajid Khan Majahar AliRaja Aqib Shamim The research method applies robust feature selection approaches to ultra-high-dimensional survival data records from Renal Cell Carcinoma patients through deep learning methodologies. The linear methods LASSO and Elastic Net encounter failure when processing data because they face simultaneous multicollinearity issues in addition to overfitting effects and produce marginal survival outcome variability prediction at 54%. We suggest combining ISIS with deep learning architectures featuring PCA-RFA-RSIS models as a remedy to handle these present limitations. Among all evaluated methods PCA-RFA-RSIS is proved most accurate with an MSE measurement of 24.39 and R2 value of 0.89. PCA improved the model’s dimensionality reduction power and robust ISIS maintained model stability despite outliers present in the data. The discovery holds significant value in precision medicine because it creates opportunities to develop individualized therapy for kidney failure patients. Further research needs to enhance hybrid models and expand their utilization between different diseases as well as complex biological systems. https://journal.nsps.org.ng/index.php/jnsps/article/view/2772Ultra-High-Dimensional Survival AnalysisRenal Cell Carcinoma (RCC)Feature Selection with Deep LearningRobust SISRobust ISIS |
| spellingShingle | Shaymaa Mohammed Ahmed Majid Khan Majahar Ali Raja Aqib Shamim Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma Journal of Nigerian Society of Physical Sciences Ultra-High-Dimensional Survival Analysis Renal Cell Carcinoma (RCC) Feature Selection with Deep Learning Robust SIS Robust ISIS |
| title | Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma |
| title_full | Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma |
| title_fullStr | Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma |
| title_full_unstemmed | Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma |
| title_short | Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma |
| title_sort | integrating robust feature selection with deep learning for ultra high dimensional survival analysis in renal cell carcinoma |
| topic | Ultra-High-Dimensional Survival Analysis Renal Cell Carcinoma (RCC) Feature Selection with Deep Learning Robust SIS Robust ISIS |
| url | https://journal.nsps.org.ng/index.php/jnsps/article/view/2772 |
| work_keys_str_mv | AT shaymaamohammedahmed integratingrobustfeatureselectionwithdeeplearningforultrahighdimensionalsurvivalanalysisinrenalcellcarcinoma AT majidkhanmajaharali integratingrobustfeatureselectionwithdeeplearningforultrahighdimensionalsurvivalanalysisinrenalcellcarcinoma AT rajaaqibshamim integratingrobustfeatureselectionwithdeeplearningforultrahighdimensionalsurvivalanalysisinrenalcellcarcinoma |