Deep Learning-Based Survival Analysis for High-Dimensional Survival Data

Lin Hao, Juncheol Kim, Sookhee Kwon and Il Do Ha
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Lin Hao: Department of Statistics, Pukyong National University, Busan 48513, Korea
Juncheol Kim: Department of Statistics, Pukyong National University, Busan 48513, Korea
Sookhee Kwon: Department of Statistics, Pukyong National University, Busan 48513, Korea
Il Do Ha: Department of Statistics, Pukyong National University, Busan 48513, Korea

Mathematics, 2021, vol. 9, issue 11, 1-18

Abstract: With the development of high-throughput technologies, more and more high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such data has become a significant challenge. Machine learning (ML) algorithms have been widely applied for modeling nonlinear and complicated interactions in a variety of practical fields such as high-dimensional survival data. Recently, 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 on the survival datasets with high-dimensional or large sample sizes. In this paper, we evaluated the prediction performance of the DNNSurv model using ultra-high-dimensional and high-dimensional survival datasets and compared it with three popular ML survival prediction models (i.e., random survival forest and the Cox-based LASSO and Ridge models). For this purpose, we also present the optimal setting of several hyperparameters, including the selection of a tuning parameter. The proposed method demonstrated via data analysis that the DNNSurv model performed well overall as compared with the ML models, in terms of the three main evaluation measures (i.e., concordance index, time-dependent Brier score, and the time-dependent AUC) for survival prediction performance.

Keywords: censored data; machine learning; deep learning; DNNSurv; survival analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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