Quick Utilization of Algorithmic Random-forest for Knowledge in Breast Cancer
This project focuses on developing a Machine Learning model to predict Breast Cancer phenotypes, with a specific emphasis on Triple-Negative Breast Cancer (TNBC). It aims to bridge the gap between clinical oncology and data science.
- Language: Python
- Key Libraries: Pandas, Scikit-Learn, Matplotlib
- Algorithm: Random Forest Classifier
Developed by Abudala Sualé, a 4th-year Medical Student at Universidade Eduardo Mondlane (UEM). This research is part of an ongoing effort to apply AI in precision medicine within the Mozambican healthcare context.
- Initial algorithm design
- Data validation and cleaning
- Final model evaluation
- Manuscript drafting
This project is licensed under the MIT License.
ORCID PROFILE: https://orcid.org/0009-0003-3055-3028