AUTHOR=Yang Hong , Tian Jing , Meng Bingxia , Wang Ke , Zheng Chu , Liu Yanling , Yan Jingjing , Han Qinghua , Zhang Yanbo 

TITLE=Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.726516

DOI=10.3389/fcvm.2021.726516

ISSN=2297-055X

ABSTRACT=Objective: To explore the application of the Cox model based on extreme learning machine in the survival analysis of patients with chronic heart failure. 
Methods: The medical records of 5279 inpatients diagnosed with chronic heart failure in two grade 3 and first-class hospitals in Taiyuan from 2014 to 2019 were collected; with death as the outcome and after the feature selection, the Lasso Cox , random survival forest (RSF), and the Cox model based on extreme learning machine (ELM Cox) were constructed for survival analysis and prediction; the prediction performance of the three models was explored based on simulated data with three censoring ratios of 25%, 50%, and 75%. 
Results: Simulation results showed that the prediction performance of the three models decreased with increasing censoring proportion, and the ELM Cox model performed best overall; the ELM Cox model constructed with 21 highly influential survival predictors screened from actual chronic heart failure data showed the best performance with C-index and Integrated Brier Score(IBS) of 0.775(0.755, 0.802) and 0.166(0.150, 0.182), respectively. 
Conclusions: The ELM Cox model showed good discrimination performance in the survival analysis of patients with chronic heart failure; it performs consistently for data with a high proportion of censored survival time; therefore, the model could help physicians identify patients at high risk of poor prognosis and target therapeutic measures to patients as early as possible.