Background: Colon cancer remains among the top perpetrators of deaths
linked to cancer. The probability of cancer reaching more parts of the body is
extremely high in colorectal cancers. Early detection is hence, highly important for
Method: In the current work, a hybrid approach toward the detection of colon cancers
through the usage of microarray datasets, is presented. Particle Swarm Optimization
(PSO) is utilized for features extraction, while Support Vector Machine (SVM) and
Bagging approaches are utilized as classifiers.
Results: The Colon Microarray Gene Dataset is used to evaluate minimum Redundancy
Maximum Relevance (mRMR), Bagging, SVM, PSO and PSO-SVM with regard to classification
accuracy, sensitivity and specificity. The proposed PSO-SVM displays best performance in all categories.
Conclusion: Experiments reveal the capabilities of the proposed PSO-SVM to explore features space for
the optimal features combination for gene selection from microarray data.