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

Yuhan Gao | Zendy; Shichong Jia | Zendy; Dihua Li | Zendy; Chao Huang | Zendy; Zhaowei Meng | Zendy; Yan Wang | Zendy; Yu Mei | Zendy; Tianyi Xu | Zendy; Ming Liu | Zendy; Jinhong Sun | Zendy; Qiyu Jia | Zendy; Qing Zhang | Zendy; Ying Gao | Zendy; Kun Song | Zendy; Xing Wang | Zendy; Yaguang Fan | Zendy

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

Author(s) -

Yuhan Gao,

Shichong Jia,

Dihua Li,

Chao Huang,

Zhaowei Meng,

Yan Wang,

Yu Mei,

Tianyi Xu,

Ming Liu,

Jinhong Sun,

Qiyu Jia,

Qing Zhang,

Ying Gao,

Kun Song,

Xing Wang,

Yaguang Fan

Publication year - 2021

Publication title -

bioscience reports

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.938

H-Index - 77

eISSN - 1573-4935

pISSN - 0144-8463

DOI - 10.1042/bsr20203859

Subject(s) - logistic regression , hyperuricemia , receiver operating characteristic , medicine , random forest , predictive value , predictive modelling , statistics , predictive power , demography , prospective cohort study , regression analysis , correlation , mathematics , uric acid , machine learning , computer science , philosophy , epistemology , sociology , geometry

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.

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