Rahman, Emad Abd Al (2025) Improving The Feature Selection, Multi-Class Classification, And Imbalanced Dataset Of Breast Cancer. PhD thesis, Perpustakaan Hamzah Sendut.
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Abstract
The fact that breast cancer is prevalent among women makes it a serious problem in global health. Its early detection is crucial for improving treatment options and increasing survival rates, yet the complexity of diagnosing and determining the most effective treatment plan presents significant challenges. Recent years have seen the rise of ai and ml techniques as powerful tools in the fight against breast cancer, opening new possibilities for improving detection and treatment methods. This research aims to address key challenges in the application of ai/ml to breast cancer treatment planning, including imbalanced datasets, suboptimal feature selection methods, and the complexity of multi-class classification tasks. The study justifies its focus by addressing the unmet need for improved computational tools that can personalize and optimize treatment strategies for breast cancer patients. We aim to enhance model performance on imbalanced datasets by improving feature selection procedures, refine multi-class classification models, and to develop predictive models for personalized treatment planning. Successful completion of the thesis's objectives will allow it to make a substantial contribution to the creation of optimal treatment plans for individual patients.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis |
| Depositing User: | Mr Hasmizar Mansor |
| Date Deposited: | 27 Apr 2026 02:44 |
| Last Modified: | 27 Apr 2026 02:44 |
| URI: | http://eprints.usm.my/id/eprint/63999 |
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