Prediction model of random forest for the risk of hyperuricemia in a Chinese basic health checkup test

Objectives: The present study aimed to develop a random forest (RF) based prediction model for hyperuricemia (HUA) and compare its performance with the conventional logistic regression (LR) model. Methods: This cross-sectional study recruited 91,690 participants (14,032 with HUA, 77,658 without HUA)...

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Detalles Bibliográficos
Autores principales: Gao, Yuhan, Jia, Shichong, Li, Dihua, Huang, Chao, Meng, Zhaowei, Wang, Yan, Yu, Mei, Xu, Tianyi, Liu, Ming, Sun, Jinhong, Jia, Qiyu, Zhang, Qing, Gao, Ying, Song, Kun, Wang, Xing, Fan, Yaguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Portland Press Ltd. 2021
Materias:
Diagnostics & Biomarkers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026814/
https://www.ncbi.nlm.nih.gov/pubmed/33749777
http://dx.doi.org/10.1042/BSR20203859

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026814/
https://www.ncbi.nlm.nih.gov/pubmed/33749777
http://dx.doi.org/10.1042/BSR20203859

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