$$Predict the Risk of Dyslipidemia via Deep Neural Networks for Survival Data$$

Jiahui Xiao

Keywords: dyslipidemia, risk prediction, deep neural network, survival analysis

TL;DR: This study applies a DNN based learning algorithm, to develop a risk prediction model for dyslipidemia based on routine check-up data.

Abstract:

Background: Dyslipidemia is an important risk factor for coronary artery disease and stroke. Early detection and prevention of dyslipidemia can markedly alter cardiovascular morbidity and mortality. Cox proportional hazard model has been commonly employed for survival datasets to construct the prediction model. Recently, the data-driven learning algorithm began to be used to analyze right-censored survival data. However, there is no attempt to use deep neural networks in dyslipidemia prediction. The objective of this study is to predict the risk of dyslipidemia via deep neural networks for survival data.
Methods: The study cohort was based on the routine health check-up data from 6,328 participants aged 19 to 90 years and free of dyslipidemia at baseline. A deep neural network (DNN) was used to develop risk models for predicting dyslipidemia. Cox Proportional Hazards (Cox) and Random Survival Forests (RSF) were applied in comparison with the DNN model. As metric of performance, we use the time-dependent concordance index (Ctd-index).
Results: The Ctd-index of the prediction models by using DNN was 0.802. The DNN model performed significantly better than Cox and RSF model (Ctd-index: 0.735 and 0.770, respectively). The improvement of DNN over the competing methods was statistically significant. Moreover, DNN provides performance gain on time intervals compared to conventional survival models.
Conclusions: DNN is a promising method in learning the estimated distribution of survival time and event while capturing the right-censored nature inherent in survival data. DNN achieves large and statistically significant performance improvements over previous intuitive regression model and state-of-the-art data-mining methods.

Submission Number: 8