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Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset
OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed fr...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967697/ https://www.ncbi.nlm.nih.gov/pubmed/36834107 http://dx.doi.org/10.3390/ijerph20043411 |
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author | Zheng, Ziwei Si, Zhikang Wang, Xuelin Meng, Rui Wang, Hui Zhao, Zekun Lu, Haipeng Wang, Huan Zheng, Yizhan Hu, Jiaqi He, Runhui Chen, Yuanyu Yang, Yongzhong Li, Xiaoming Xue, Ling Sun, Jian Wu, Jianhui |
author_facet | Zheng, Ziwei Si, Zhikang Wang, Xuelin Meng, Rui Wang, Hui Zhao, Zekun Lu, Haipeng Wang, Huan Zheng, Yizhan Hu, Jiaqi He, Runhui Chen, Yuanyu Yang, Yongzhong Li, Xiaoming Xue, Ling Sun, Jian Wu, Jianhui |
author_sort | Zheng, Ziwei |
collection | PubMed |
description | OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: 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. RESULTS: 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, 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. CONCLUSION: The 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. |
format | Online Article Text |
id | pubmed-9967697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99676972023-02-27 Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset Zheng, Ziwei Si, Zhikang Wang, Xuelin Meng, Rui Wang, Hui Zhao, Zekun Lu, Haipeng Wang, Huan Zheng, Yizhan Hu, Jiaqi He, Runhui Chen, Yuanyu Yang, Yongzhong Li, Xiaoming Xue, Ling Sun, Jian Wu, Jianhui Int J Environ Res Public Health Article OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: 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. RESULTS: 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, 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. CONCLUSION: The 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. MDPI 2023-02-15 /pmc/articles/PMC9967697/ /pubmed/36834107 http://dx.doi.org/10.3390/ijerph20043411 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zheng, Ziwei Si, Zhikang Wang, Xuelin Meng, Rui Wang, Hui Zhao, Zekun Lu, Haipeng Wang, Huan Zheng, Yizhan Hu, Jiaqi He, Runhui Chen, Yuanyu Yang, Yongzhong Li, Xiaoming Xue, Ling Sun, Jian Wu, Jianhui Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset |
title | Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset |
title_full | Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset |
title_fullStr | Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset |
title_full_unstemmed | Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset |
title_short | Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset |
title_sort | risk prediction for the development of hyperuricemia: model development using an occupational health examination dataset |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967697/ https://www.ncbi.nlm.nih.gov/pubmed/36834107 http://dx.doi.org/10.3390/ijerph20043411 |
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