Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure

Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study used data of multiple environmental chemical exposures from the 2011–2012 cycle of the National Health and Nutrition Examination Survey (NHANES) to develop an interpretable machine learning model for h...

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Bibliographic Details
Main Authors: Xiaochuan Lu, Huawei Kou, Cong Li, Runqing Zhan, Rongrong Guo, Shengnan Liu, Peixuan Shen, Meiyue Shen, Tingwei Du, Jiaqi Lu, Xiaoli Shen
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Ecotoxicology and Environmental Safety
Subjects:
Hyperuricemia
Environmental chemicals
Machine learning
Extreme Gradient Boosting
Shapley additive explanations
Partial dependence plots
Online Access:http://www.sciencedirect.com/science/article/pii/S0147651325007286
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