Prediction of Second Primary Lung Cancer Patient’s Survivability Based on Improved Eigenvector Centrality-Based Feature Selection-Reference-Cited by-同舟云学术

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Prediction of Second Primary Lung Cancer Patient’s Survivability Based on Improved Eigenvector Centrality-Based Feature Selection

Published:2021 Issue: Volume:9 Page:55663-55672
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Liu Peng^ORCID,Jin Kexin^ORCID,Jiao Yiping,He Mutian^ORCID,Fei Shumin^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09370095.pdf?arnumber=9370095

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lung cancer survival prognosis using a two-stage modeling approach;Multimedia Tools and Applications;2024-01-31

2. A framework to predict second primary lung cancer patients by using ensemble models;Annals of Operations Research;2023-12-14

3. Classification Prediction of Lung Cancer Based on Machine Learning Method;International Journal of Healthcare Information Systems and Informatics;2023-11-15

4. Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment;Computer Methods in Biomechanics and Biomedical Engineering;2023-10-04

5. Artificial Intelligence Based Lung Cancer Prediction Using Convolutional Neural Network Algorithm;2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC);2023-06-16

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