A novel diagnosis method for SZ by deep neural networks

Abstract

Single nucleotide polymorphism (SNP) data are typical high-dimensional and low-sample size (HDLSS) data, and they are extremely complex. In this paper, by using a deep neural network with a loci filter method, multi-level abstract features of SNPs data are obtained. Based on the abstract features, we get the diagnosis results for schizophrenia. It shows that the performance of the deep network is better than those of other methods, i.e., linear SVM with soft margin, SVM with multilayer perceptron kernel, SVM with RBF kernel, sparse representation based classifier and k-nearest neighbor method. These results indicate that the use of deep networks offers a novel approach to deal with HDLSS problem, especially for the medical data analysis.