scholarly journals Extreme learning machine Cox model for high‐dimensional survival analysis

2019 ◽  
Vol 38 (12) ◽  
pp. 2139-2156 ◽  
Author(s):  
Hong Wang ◽  
Gang Li
Keyword(s):  
Survival Analysis ◽  
Extreme Learning Machine ◽  
Cox Model ◽  
High Dimensional ◽  
Learning Machine

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

2021 ◽  
Vol 8 ◽  
Author(s):  
Hong Yang ◽  
Jing Tian ◽  
Bingxia Meng ◽  
Ke Wang ◽  
Chu Zheng ◽  
...  
Keyword(s):  
Heart Failure ◽  
Survival Analysis ◽  
Chronic Heart Failure ◽  
Survival Time ◽  
Extreme Learning Machine ◽  
Cox Model ◽  
Prediction Performance ◽  
Brier Score ◽  
Model Based ◽  
Learning Machine

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 5,279 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.Conclusion: 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.

Two-stage extreme learning machine for high-dimensional data

2014 ◽  
Vol 7 (5) ◽  
pp. 765-772 ◽  
Author(s):  
Peng Liu ◽  
Yihua Huang ◽  
Lei Meng ◽  
Siyuan Gong ◽  
Guopeng Zhang
Keyword(s):  
Extreme Learning Machine ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Two Stage ◽  
Learning Machine

Extreme Learning Machine with Harmony Search for High Dimensional Data Classifications

2021 ◽  
Author(s):  
Sicheng Li ◽  
Zhihong Man ◽  
Zhenwei Cao ◽  
Jinchuan Zheng ◽  
Yuan Chen
Keyword(s):  
Extreme Learning Machine ◽  
High Dimensional Data ◽  
Harmony Search ◽  
High Dimensional ◽  
Learning Machine
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