{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["20(4)"],"submitter":["Zheng Z"],"pubmed_abstract":["<h4>Objective</h4>Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic.<h4>Methods</h4>We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability.<h4>Results</h4>The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6,"],"journal":["International journal of environmental research and public health"],"pagination":["3411"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9967697"],"repository":["biostudies-literature"],"pubmed_title":["Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset."],"pmcid":["PMC9967697"],"pubmed_authors":["Zheng Z","Wu J","Zheng Y","Meng R","Wang H","Zhao Z","He R","Li X","Yang Y","Lu H","Hu J","Si Z","Chen Y","Xue L","Wang X","Sun J"],"additional_accession":[]},"is_claimable":false,"name":"Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset.","description":"<h4>Objective</h4>Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic.<h4>Methods</h4>We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability.<h4>Results</h4>The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6,","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-05-30T03:07:39.397Z","creation":"2025-02-18T23:57:41.937Z"},"accession":"S-EPMC9967697","cross_references":{"pubmed":["36834107"],"doi":["10.3390/ijerph20043411"]}}