Mahmood Khalsan^1,2, Mu Mu¹, Eman Salih Al-shamery¹, Suraj Ajit¹, Lee Machado¹, and Michael Opoku Agyeman¹, ¹The University of Northampton, UK, ²University of Babylon, UK

In the realm of cancer research, the identification of biomarker genes plays a pivotal role in accurate classification and diagnosis. This study delves into the intersection of machine learning and gene selection to enhance the precision of biomarker identification for cancer classification. Leveraging advanced computational techniques. In the quest for improved cancer classification, studies face challenges due to high-dimensional gene expression data and limited gene relevance. To address these challenges, we developed a novel multidimensional fuzzy deep learning (MFDL) to select subset of significant genes and using those genes to train the model for better accuracy. MFDL is exploring the integration of fuzzy concepts within filter and wrapper methods to select significant genes and applying a fuzzy classifier to improve cancer classification accuracy. Through rigorous experimentation and validation, six gene expression data used, the findings demonstrated the efficacy of our methodology on diverse cancer datasets. The results underscore the importance of integrative computational methods in deciphering the intricate genomic landscape of cancer and spotlight the potential for improved diagnostic accuracy. The developed model showcased outstanding performance across the six employed datasets, demonstrating an average accuracy of 98%, precision of 98.3%, recall of 97.6%, and an f1-score of 97.8%.

Deep learning, Gene selection, Cancer classification , Gene expression