Prominent Gene Selection and Classification of Colorectal Cancer using Microarray Data

The description of gene intonation can be exploited in the determination of acute disorder like carcinoma. The picking of biological genes is substantial as well as vital for cancer observation. This paper offers an outline suggesting two phases of gene selection for colon data categorization. Colorectal tumor positions third among extensive cancer and foremost reason of cancer demise globally. There is a necessity to realize exact process to recognize the tissues and to improve the diagnostic method for discovering medicines. Freely accessible binary class colon data that embraces 2,000 genes and 62 trials is used in this research. The two-phase feature selection includes Analysis of Variance test as first phase and in second phase, the Hilbert transform and Firefly algorithm are applied to get pertinent genes. Then finally, the obtained colon data are classified as normal and tumor classes using Linear Regression, Logistic Regression, Naïve Bayes and Harmonic search classifiers. The results indicate that the Harmonic search classifier provides a greater accuracy of 95.06% for Hilbert transformed colon tumor features and an accuracy of 90.37% for Hilbert transformed colon normal features. For Firefly clusters, the naive bayes classifier attains a higher accuracy of 93.36% and 91.93% respectively for colon tumor and normal data. It is deduced that the performance of two-phase gene selection method with Harmonic search and Naïve bayes classifier has accomplished a better accuracy in contrast with the other classifiers employed in this paper.