Survival parametric modeling for patients with heart failure based on Kernel learning.

Abstract

Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by injecting non-linearity into linear models. In this study, a Multiple Kernel Learning (MKL) method has been proposed to optimize survival outcomes under the Accelerated Failure Time (AFT) model, a useful alternative to the Proportional Hazards (PH) frailty model. In other words, a survival parametric regression framework has been presented for clinical data to effectively integrate kernel learning with AFT model using a gradient descent optimization strategy. This methodology involves applying four different parametric models, evaluated using 19 distinct kernels to extract the best fitting scenario. This culminated in a sophisticated strategy that combined these kernels through MKL. We conducted a comparison between the Frailty model and MKL due to their shared fundamental properties. The models were assessed using the Concordance index (C-index) and Brier score (B-score). Each model was tested on both a case study and a replicated/independent dataset. The outcomes showed that kernelization enhances the performance of the model, especially by combining selected kernels for MKL.