ArticleInternational journal of environmental research and public health2023
Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Associations between multiple essential trace metal concentrations and risk of hyperuricemia: insights from a central Chinese population.Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine · 2026Article
- Global Trends and Research Hotspots in Natural Products for Hyperuricemia: A Bibliometric Analysis (2004-2026).Pharmaceuticals (Basel, Switzerland) · 2026Review
- Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction.Statistical methods in medical research · 2025Article
- Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.BMC medical informatics and decision making · 2025Article
- Punicalagin attenuates hyperuricemia via restoring hyperuricemia-induced renal and intestinal dysfunctions.Journal of advanced research · 2025Article
- Screening and evaluation of purines-degrading lactic acid bacteria isolated from traditional fermented foods in Yunnan Province and their uric acid-lowering effectsFrontiers in microbiology · 2025Article
- Predicting the risk of subclinical atherosclerosis based on interpretable machine models in a Chinese T2DM population.Frontiers in endocrinology · 2024Article
- Study on risk factor analysis and model prediction of hyperuricemia in different populations.Frontiers in nutrition · 2024Article
- Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach.Journal of inflammation research · 2024Article
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17 authors.
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Abstract
objectiveHyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic.
methodsWe 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.
resultsThe training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6, sensitivity was 68.4, 72.3, and 81.5, specificity was 82.0, 85.7, and 86.8, the area under the ROC curve was 0.734, 0.724, and 0.806, and Brier score was 0.121, 0.194, and 0.095, respectively. The XG Boost model effect evaluation index was better than the other two models, and similar results were obtained in the validation set. In terms of clinical applicability, the XG Boost model had higher clinical applicability than the Logistic regression and CNN models.
conclusionThe prediction effect of the XG Boost model was better than the CNN and Logistic regression models and was suitable for the prediction of HUA onset risk in steelworkers.
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