Variable selection methods based on variable importance measurement from random forest and its application in diagnosis of tumor typing

WANG Wen-jie; MA Jin-sha; GAO Qian; WANG Tong

doi:10.16462/j.cnki.zhjbkz.2023.03.005

Volume 27 Issue 3

Mar. 2023

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(3): 274-280

WANG Wen-jie, MA Jin-sha, GAO Qian, WANG Tong. Variable selection methods based on variable importance measurement from random forest and its application in diagnosis of tumor typing[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(3): 274-280. doi: 10.16462/j.cnki.zhjbkz.2023.03.005

Citation:

WANG Wen-jie, MA Jin-sha, GAO Qian, WANG Tong. Variable selection methods based on variable importance measurement from random forest and its application in diagnosis of tumor typing[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(3): 274-280. doi: 10.16462/j.cnki.zhjbkz.2023.03.005

Citation:

WANG Wen-jie, MA Jin-sha, GAO Qian, WANG Tong. Variable selection methods based on variable importance measurement from random forest and its application in diagnosis of tumor typing[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(3): 274-280. doi: 10.16462/j.cnki.zhjbkz.2023.03.005

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Variable selection methods based on variable importance measurement from random forest and its application in diagnosis of tumor typing

doi: 10.16462/j.cnki.zhjbkz.2023.03.005

Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China

Funds:

National Natural Science Foundation of China 81872715

National Natural Science Foundation of China 82073674

Major Science and Technology Project of Shanxi Province 202005D121008

Major Science and Technology Project of Shanxi Province 202102130501003

More Information

Corresponding author: WANG Tong, E-mail: tongwang@sxmu.edu.cn
Received Date: 2022-02-18
Rev Recd Date: 2022-05-23

Available Online: 2023-04-04

Publish Date: 2023-03-10

Abstract

Abstract

Objective To explore the variable selecting methods based on variable importance measurement from random forest (RF) for binary outcome in the high-dimensional omics data, and to choose the appropriate methods to construct the outcome prediction model. Methods First, according to the different variable selection objectives, we simulated and compared the ability of minimum optimized variable selection RF methods [recursive feature elimination (RFE)-RF, biosigner] and all relevant variable selection RF methods (Boruta, vita, altmann and r2vim) to identify important variables in high-dimensional data. Then we combined different methods to select genes related to diffuse large B cell lymphoma (DLBCL) classification and constructed the model for diffuse large B cell lymphoma classification diagnosis. Results Simulation study showed that vita had higher sensitivity, and biosigner had higher positive predictive value. Empirical study showed that a total of 1 019 genes related to DLBCL classification were obtained by vita method, and 77 genes related to DLBCL classification were obtained by biosigner method. The area under the receiver operating characteristical (ROC) curve (AUC) of the DLBCL typing diagnostic model was 0.910. Conclusions Vita and biosigner can be used in the preliminary and final selecting stages of genes related to DLBCL classification. The model we developed can effectively distinguish the different subtypes of DLBCL.
- Random forest,
- Variable selection,
- Diffuse large B-cell lymphoma

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References(26)

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