Hybrid Soft Computing Approach for Prediction of Cancer in Colon Using Microarray Gene Data

Abstract

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 faster treatment. 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. Keywords: Colon cancer, microarray data, Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Bagging, minimum Redundancy Maximum Relevancy (mRMR), beam search.