Design and implementation of cancer patient survival prediction model based on ensemble learning method

Design and implementation of cancer patient survival prediction model based on ensemble learning method

To study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database. Firstly, according to the significant expression and stability test, the long-chain non-cod...

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Main Authors: Chuanmei Bi, Xiaonan Fang, Zezheng Geng, Ling Dong
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/47/e3sconf_icepe2021_04030.pdf
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spelling doaj-77df441e90344cceb811dae78b84d0ad2021-06-18T08:20:28ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012710403010.1051/e3sconf/202127104030e3sconf_icepe2021_04030Design and implementation of cancer patient survival prediction model based on ensemble learning methodChuanmei Bi0Xiaonan Fang1Zezheng Geng2Ling Dong3SHANDONG MANAGEMENT UNIVERSITYSHANDONG MANAGEMENT UNIVERSITYSHANDONG MANAGEMENT UNIVERSITYSHANDONG MANAGEMENT UNIVERSITYTo study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database. Firstly, according to the significant expression and stability test, the long-chain non-coding RNA in the transcriptome that has a significant impact on clinical prognosis survival analysis was initially screened. Then use a random forest ensemble learning algorithm to train it to get a preliminary model. Finally, based on the optimized random survival forest model, the Cox regression model was once again integrated, and the risk values of the two were integrated. The RCCT (Random-Cox Combined to Survival) method was proposed to provide clinical decision-makers certain reference values.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/47/e3sconf_icepe2021_04030.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Chuanmei Bi
Xiaonan Fang
Zezheng Geng
Ling Dong
spellingShingle Chuanmei Bi
Xiaonan Fang
Zezheng Geng
Ling Dong
Design and implementation of cancer patient survival prediction model based on ensemble learning method
E3S Web of Conferences
author_facet Chuanmei Bi
Xiaonan Fang
Zezheng Geng
Ling Dong
author_sort Chuanmei Bi
title Design and implementation of cancer patient survival prediction model based on ensemble learning method
title_short Design and implementation of cancer patient survival prediction model based on ensemble learning method
title_full Design and implementation of cancer patient survival prediction model based on ensemble learning method
title_fullStr Design and implementation of cancer patient survival prediction model based on ensemble learning method
title_full_unstemmed Design and implementation of cancer patient survival prediction model based on ensemble learning method
title_sort design and implementation of cancer patient survival prediction model based on ensemble learning method
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2021-01-01
description To study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database. Firstly, according to the significant expression and stability test, the long-chain non-coding RNA in the transcriptome that has a significant impact on clinical prognosis survival analysis was initially screened. Then use a random forest ensemble learning algorithm to train it to get a preliminary model. Finally, based on the optimized random survival forest model, the Cox regression model was once again integrated, and the risk values of the two were integrated. The RCCT (Random-Cox Combined to Survival) method was proposed to provide clinical decision-makers certain reference values.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/47/e3sconf_icepe2021_04030.pdf
work_keys_str_mv AT chuanmeibi designandimplementationofcancerpatientsurvivalpredictionmodelbasedonensemblelearningmethod
AT xiaonanfang designandimplementationofcancerpatientsurvivalpredictionmodelbasedonensemblelearningmethod
AT zezhenggeng designandimplementationofcancerpatientsurvivalpredictionmodelbasedonensemblelearningmethod
AT lingdong designandimplementationofcancerpatientsurvivalpredictionmodelbasedonensemblelearningmethod
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