A novel approach for automatic gene selection and classification of gene based colon cancer datasets

Colon cancer heavily changes the composition of human genes (expressions). The deviation in the chemical composition of genes can be exploited to automatically diagnose colon cancer. The major challenge in the analysis of human gene based datasets is their large dimensionality. Therefore, efficient techniques are needed to select discerning genes. In this research article, we propose a novel classification technique that exploits the variations in gene expressions for classifying colon gene samples into normal and malignant classes, and quite intelligently tackles the larger dimensionality of gene based datasets. Previously individual feature selection techniques have been used for selection of discerning gene expressions, however, their performance is limited. In this research study, we propose a feed forward gene selection technique, wherein, two feature selection techniques are used one after the other. The genes selected by the first technique are fed as input to the second feature selection technique that selects genes from the given gene subset. The selected genes are then classified by using linear kernel of support vector machines (SVM). The feed forward approach of gene selection has shown improved performance. The proposed technique has been tested on three standard colon cancer datasets, and improved performance has been observed. It is observed that feed forward method of gene selection substantially reduces the size of gene based datasets, thereby reducing the computational time to a great extent. Performance of the proposed technique has also been compared with existing techniques of colon cancer diagnosis, and improved performance has been observed. Therefore, we hope that the proposed technique can be effectively used for diagnosis of colon cancer.