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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Haddou Bouazza, Jihad
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Optimized colon cancer classification via feature selection and machine learning Haddou Bouazza, Sara; Haddou Bouazza, Jihad
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9270

Abstract

The increasing dimensionality of gene expression data poses significant challenges in cancer classification, particularly in colon cancer. This study presents a novel filtering approach (FA) and a gene classifier (GC) to enhance gene selection and classification accuracy. Utilizing a dataset of 62 samples, our methods integrate statistical measures and machine learning classifiers, achieving classification accuracies of 96% and 97%, respectively. The FA effectively filters out noise and redundancy, allowing for accurate predictions with a minimal subset of genes, while the GC leverages multiple classifiers for optimal performance. These findings underscore the importance of robust feature selection in improving cancer diagnostics and suggest potential applications in personalized medicine. By addressing the limitations of existing methodologies, our work lays the groundwork for future research in cancer genomics, emphasizing the need for adaptive strategies to handle complex datasets.
A two-step intelligent framework for gene expression-based cancer diagnosis Haddou Bouazza, Sara; Haddou Bouazza, Jihad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4731-4738

Abstract

DNA microarray technology has advanced cancer diagnosis by enabling large-scale gene expression analysis, yet challenges remain in selecting relevant genes and achieving accurate classification. This study introduces two novel methods: the three-stage gene selection (3SGS) method and the statistics classifier (SC). By eliminating redundant, noisy, and less informative genes, the 3SGS method effectively lowers the dimensionality of gene expression data, while the SC classifier uses statistical measures of gene expression to classify samples with high accuracy and speed. Evaluated on leukemia, prostate cancer, and colon cancer datasets, the 3SGS method effectively identified minimal yet informative gene subsets, achieving 100% accuracy for leukemia, 99.3% for prostate cancer, and 97% for colon cancer. The SC classifier consistently outperformed traditional models in both accuracy and computational efficiency, completing predictions in under 2 seconds per dataset. Compared to conventional classifiers, it requires no parameter tuning and performs reliably even with small gene sets. While promising, future work should address multiclass classification and clinical validation to broaden the framework’s applicability. Together, these methods offer a precise and rapid cancer classification framework, supporting early diagnosis and personalized treatment strategies across diverse cancer types.
Co-Authors Haddou Bouazza, Sara