A comparison of cancer classification methods based on microarray data.

Abstract

Cancer is among the leading causes of death in both developed and developing countries. Through gene expression profiling of tumors, the accuracy of cancer classification has been enhanced, leading to correct diagnoses and the application of effective therapies. Here, we discuss a comparative review of the binary class predictive ability of seven classification methods (support vector machines, with the radial basis kernel (SVM(RK)), linear kernel (SVM(LK)) and the polynomial kernel (SVM(PK)), artificial neural networks (ANN), random forests (RF), k-nearest neighbor (KNN), and naive Bayes (NB)), using publicly-available gene expression data from cancer research. Results indicate that NB outperformed the other methods in terms of the accuracy, sensitivity, specificity, kappa coefficient, area under the curve (AUC), and balanced error rate (BER) of the binary classifier. Thus, overall the Naive Bayes (NB) approach turned out to be the best classifier with our datasets.