Application of Kernel-based Learning Algorithms in Survival Analysis: A Systematic Review

AbstractBackgroundThe time until an event happens is the outcome variable of interest in the statistical data analysis method known as survival analysis. Some researchers have created kernel statistics for various types of data and kernels that allow the association of a set of markers with survival data. Multiple Kernel Learning (MKL) is often considered a linear or convex combination of multiple kernels. This paper aims to provide a comprehensive overview of the application of kernel learning algorithms in survival analysis.MethodsWe conducted a systematic review which involved an extensive search for relevant literature in the field of biomedicine. After using the keywords in literature searching, 435 articles were identified based on the title and abstract screening.ResultIn this review, out of a total of 56 selected articles, only 20 articles that have used MKL for high-dimensional data, were included. In most of these articles, the MKL method has been expanded and has been introduced as a novel method. In these studies, the extended MKL models due to the nature of classification or regression have been compared with SVM, Cox PH (Cox), Extreme Learning (ELM), MKCox, Gradient Boosting (GBCox), Parametric Censored Regression Models (PCRM), Elastic-net Cox (EN-Cox), LASSO-Cox, Random Survival Forests (RSF), and Boosting Concordance Index (BoostCI). In most of these articles, the optimal model’s parameters are estimated by 10-fold cross-validation. In addition, the Concordance index (C-index) and the area under the ROC curve (AUC) were calculated to quantitatively measure the performance of all methods for validation. Predictive accuracy is improved by using kernels.ConclusionOur findings suggest that using multiple kernels instead of one single kernel can make decision functions more interpretable and can improve performance.