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Predicting risk of coronary heart disease using a teaching–learning seagull optimization algorithm with the lightboost based gradient boosting machine

Crossref DOI link: https://doi.org/10.1007/s13042-025-02719-5

Published Online: 2025-06-29

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Rao, Congjun

Lian, Jingyi

Wen, Jianghui

Chen, Lin
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (72071150, 52272354)

Fundamental Research Funds for the Central Universities project under Grant (104972024KFYjc0069)
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Text and Data Mining valid from 2025-06-29

Version of Record valid from 2025-06-29
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Article History

Received: 29 March 2024

Accepted: 17 June 2025

First Online: 29 June 2025

Declarations

:

: The authors declare no competing interests.

: None of the authors conducted studies involving human or animal participants for this article.

: The authors confirm that the final version of the manuscript has been reviewed, approved, and consented for publication by all authors.

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