Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time - Yang, Hong; Tian, Jing; Meng, Bingxia; Wang, Ke; Zheng, Chu; Liu, Yanling; Yan, Jingjing; Han, Qinghua; Zhang, Yanbo (Frontiers in Cardiovascular Medicine, 2021)
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.