Oriented Feature Selection SVM Applied to Cancer Prediction in Precision Medicine

Advances in the gene sequencing technology and the outbreak of artificial intelligence have made precision medicine a reality recently. Applying machine learning algorithms to cancer prediction using gene expression data helps to discover the link between genetic data and cancer, which will promote the development and application of precision medicine. Considering the natural order of genes, a new classification method that combines fused lasso and elastic net as regularization for linear support vector machine (SVM), which uses huberized hinge loss as the loss function, is proposed in this paper, which we name it oriented feature selection SVM (OFSSVM). Due to the characteristics of the elastic net and fused lasso, the OFSSVM can not only provide automatic feature selection, but also average the adjacent coefficients, resulting in a sparse and smooth solution. We demonstrate its effectiveness in both binary classification and multiclass classification in the sense of comprehensive evaluation that not only the classification accuracy but also the interpretability are considered. The experiments show that the OFSSVM is an appealing compromise between interpretability and classification accuracy, and is superior to other traditional methods in the sense of comprehensive evaluation.