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

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.

Download Full-text

Extreme learning machine Cox model for high‐dimensional survival analysis

Statistics in Medicine ◽

10.1002/sim.8090 ◽

2019 ◽

Vol 38 (12) ◽

pp. 2139-2156 ◽

Cited By ~ 1

Author(s):

Hong Wang ◽

Gang Li

Keyword(s):

Survival Analysis ◽

Extreme Learning Machine ◽

Cox Model ◽

High Dimensional ◽

Learning Machine

Download Full-text

A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽

10.3390/en14051328 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1328

Author(s):

Jianguo Zhou ◽

Shiguo Wang

Keyword(s):

Prediction Model ◽

Extreme Learning Machine ◽

Influencing Factors ◽

Search Algorithm ◽

Carbon Price ◽

Carbon Trading ◽

Price Prediction ◽

Model Based ◽

Mode Decomposition ◽

Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

Download Full-text

Development of a diagnostic model focusing on nutritional indicators for frailty classification in people with chronic heart failure

European Journal of Cardiovascular Nursing ◽

10.1093/eurjcn/zvab080 ◽

2021 ◽

Author(s):

Yiqin Gu ◽

Chaofeng Li ◽

Jing Yan ◽

Guoping Yin ◽

Guilan Lu ◽

...

Keyword(s):

Heart Failure ◽

Folic Acid ◽

Chronic Heart Failure ◽

Nutritional Status ◽

Functional Class ◽

Brier Score ◽

Diagnostic Model ◽

Serum Samples ◽

Frailty Status ◽

C Statistic

Abstract Aims Frailty has a great impact on the quality of life of patients with chronic heart failure (CHF), which needs to be judged in time. To develop a diagnostic model based on nutritional indicators to judge the frailty status of patients with chronic heart failure (Frailty-CHF). Methods and results In the data collection part of this study, questionnaire method and biomedical measurement method were adopted. The trace elements in serum samples were detected by high performance liquid chromatography, chemiluminescence, and inductively coupled plasma mass spectrometry. We used Excel for data consolidation, and then imported the data into R software for modelling. Lasso method was used for variable screening, and Logistics regression fitting model was used after variables were determined. The internal validation of the model was completed by Bootstrap re-sampling. A total of 123 patients were included in this study. After variables’ screening, age, nutritional status-heart failure, New York Heart Association Functional Class (NYHA), micronutrients B12, Ca, folic acid, and Se were included in the model, the c statistic and Brier score of the original model were 0.9697 and 0.0685, respectively. After Bootstrap re-sampling adjustment, the c statistic and Brier score were 0.8503 and 0.1690. Conclusion In this study, a diagnostic model of age, nutritional status-heart failure, NYHA, the micronutrients B12, Ca, folic acid, and Se was established. It could help healthcare professionals better identify the frailty status in patients with CHF.

Download Full-text