Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset

Ziwei Zheng; Zhikang Si; Xuelin Wang; Rui Meng; Hui Wang; Zekun Zhao; Haipeng Lu; Huan Wang; Yizhan Zheng; Jiaqi Hu; Runhui He; Yuanyu Chen; Yongzhong Yang; Xiaoming Li; Ling Xue; Jian Sun; Jianhui Wu

Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset

2023

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.

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