Integration of Random Forest and Hough Transform for Cancer Classification Using Microarray Gene Expression Data

Abstract: Cancer classification poses a significant challenge owing to the intricate nature and diversity of the disease. This study introduces a novel methodology for cancer classification leveraging microarray gene expression data. The proposed approach integrates Random Forest (RF) and Hough Transform (HT), where RF performs feature selection and classification, and HT identifies sub-biclusters that are merged into larger clusters using a hypergraph model. Evaluation on multiple cancer datasets demonstrates that the hybrid approach improves classification accuracy compared to standalone RF or HT while capturing meaningful gene expression patterns.

Hibah Alatawi and Hechmi Shili. “Integration of Random Forest and Hough Transform for Cancer Classification Using Microarray Gene Expression Data”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170553

@article{Alatawi2026,
title = {Integration of Random Forest and Hough Transform for Cancer Classification Using Microarray Gene Expression Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170553},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170553},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Hibah Alatawi and Hechmi Shili}
}