基于super learner算法的集成学习及其在纵向删失数据预测建模中的应用

doi:10.3969/j.issn.1006-5253.2021.01.021

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Abstract

Objective Ensemble learning is a novel approach to improving learning accuracy in machine learning field recently. This paper aims to introduce the application of ensemble learning method based on super learner algorithm in the prediction modeling of longitudinal censored data and its implementation of R language. Methods This paper introduced the principle in modeling longitudinal censored data based on super learner algorithm and its implementation method with R-programming language. In addition, tumor survival data from TCGA database were used for real data analysis to illustrate its performance in practice. Results The estimation methods for model parameters and definition of ensemble learning parameters based on super learner algorithm are more flexible. In actual data analysis, super learner algorithm can make full use of the obtained data to establish the prediction model. The prediction accuracy of the model is 0.8737 (95% CI: 0.7897-0.9330) and the C-index is 0.883, so the prediction performance is good. Conclusion The ensemble learning approach with super learner algorithm provides a new choice for the prediction analysis based on longitudinal censored data.

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ensemble learning super learner prediction model longitudinal censored data R-programming language

Received: 19 October 2020

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	Articles by authors
	Yang Yuhui
	Wang Jingxian
	Zhao Peng
	Li Yemian
	Chen Fangyao.

Cite this article:

Yang Yuhui,Wang Jingxian,Zhao Peng, et al.

Ensemble learning based on super learner algorithm and its application in prediction modeling for longitudinal censored data

[J]. journal1, 2021, 28(1): 86-90.

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http://manu58.magtech.com.cn/Jwk_zgyytj/EN/10.3969/j.issn.1006-5253.2021.01.021 OR http://manu58.magtech.com.cn/Jwk_zgyytj/EN/Y2021/V28/I1/86

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