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Buckley-James boosting model based on extreme learning machine and random survival forests.

Jianfen Kong1, Shuhong Zhang1

  • 1School of Mathematics and Statistics, Center for Data Science, Lanzhou University, Lanzhou, P.R.China.

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|April 17, 2023
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Summary

This study introduces a novel ELM-based Buckley-James boosting model for survival data. This advanced model effectively captures both linear and nonlinear covariate effects, outperforming traditional methods.

Keywords:
Buckley-James modelensemble learningextreme learning machinerandom survival forestssurvival analysis

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Survival Analysis

Background:

  • The Buckley-James (BJ) model is a semiparametric accelerated failure time model.
  • Traditional BJ models assume linearity, limiting their ability to handle complex nonlinear relationships in survival data.

Purpose of the Study:

  • To develop a novel regression model for right-censored survival data that overcomes the linearity limitations of the traditional BJ model.
  • To integrate machine learning techniques for improved handling of nonlinear covariate effects.

Main Methods:

  • The proposed method, the ELM-based BJ boosting model, utilizes random survival forests (RSF) for covariate imputation.
  • It employs an ensemble of extreme learning machines (ELMs) within an L2 boosting framework for regression.
  • The ELM-based boosting model's output replaces the linear combination of covariates in the BJ model.

Main Results:

  • The ELM-based BJ boosting model demonstrated superior performance compared to traditional BJ models, BJ boosting variants, RSF, and the Cox proportional hazards model.
  • Performance was evaluated using concordance index and integrated Brier score in simulation studies and real-world data.

Conclusions:

  • The ELM-based BJ boosting model effectively captures both linear and nonlinear covariate effects in survival data.
  • This novel approach offers significant improvements in predictive accuracy for right-censored survival data analysis.