Computational Intelligent Techniques for Tumor Classification (Using Microarray Gene Expression Data)

Computational intelligent techniques can be useful at the diagnosis stage to assist the Oncologist in identifying the malignancy of a tumor. In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computational intelligent techniques for classification accuracy on selected datasets. Classification accuracy is evaluated with Linear genetic Programs, Multivariate Regression Splines (MARS), Classification and Regression Tress (CART) and Random Forests. We analyze both type of errors false positives and false negatives on four datasets. Linear Genetic Programs and Random forests perform the best for detecting malignancy of different tumors. Our results demonstrate the potential of using learning machines in diagnosis of the malignancy of a tumor. The classifiers used perform the best using the most significant features expect for Prostate cancer dataset.