Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning-based Survival Analysis for High-dimensional Survival Data

Version 1 : Received: 19 April 2021 / Approved: 20 April 2021 / Online: 20 April 2021 (11:15:02 CEST)

How to cite: Ha, I.D.; Hao, L.; Kim, J.; Kwon, S. Deep Learning-based Survival Analysis for High-dimensional Survival Data. Preprints 2021, 2021040529 (doi: 10.20944/preprints202104.0529.v1). Ha, I.D.; Hao, L.; Kim, J.; Kwon, S. Deep Learning-based Survival Analysis for High-dimensional Survival Data. Preprints 2021, 2021040529 (doi: 10.20944/preprints202104.0529.v1).

Abstract

As the development of high-throughput technologies, more and more high-dimensional or ultra high-dimensional genomic data are generated. Therefore, how to make effective analysis of such data becomes a challenge. Machine learning (ML) algorithms have been widely applied for modelling nonlinear and complicated interactions in a variety of practical fields such as high-dimensional survival data. Recently, the multilayer deep neural network (DNN) models have made remarkable achievements. Thus, a Cox-based DNN prediction survival model (DNNSurv model) , which was built with Keras and Tensorflow, was developed. However, its results were only evaluated to the survival datasets with high-dimensional or large sample sizes. In this paper, we evaluate the prediction performance of the DNNSurv model using ultra high-dimensional and high-dimensional survival datasets, and compare it with three popular ML survival prediction models (i.e., random survival forest and Cox-based LASSO and Ridge models). For this purpose we also present the optimal setting of several hyper-parameters including selection of tuning parameter. The proposed method demonstrates via data analysis that the DNNSurv model performs overall well as compared with the ML models, in terms of three main evaluation measures (i.e., concordance index, time-dependent Brier score and time-dependent AUC) for survival prediction performance.

Subject Areas

censored data; machine learning; deep learning; DNNSurv; survival analysis

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