Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population
Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors.Methods: A prospective health checkup cohort of 40899 subjects was examined an...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2024.1357404/full |
| _version_ | 1797214687665848320 |
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| author | Yongsheng Zhang Yongsheng Zhang Yongsheng Zhang Li Zhang Haoyue Lv Haoyue Lv Haoyue Lv Guang Zhang Guang Zhang Guang Zhang |
| author_facet | Yongsheng Zhang Yongsheng Zhang Yongsheng Zhang Li Zhang Haoyue Lv Haoyue Lv Haoyue Lv Guang Zhang Guang Zhang Guang Zhang |
| author_sort | Yongsheng Zhang |
| collection | DOAJ |
| description | Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors.Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors.Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model’s superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model’s applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846.Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas. |
| first_indexed | 2024-04-24T11:18:08Z |
| format | Article |
| id | doaj.art-713a26a6dd124a5a9432d093c1ce677b |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-24T11:18:08Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-713a26a6dd124a5a9432d093c1ce677b2024-04-11T05:08:59ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2024-04-011510.3389/fphys.2024.13574041357404Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup populationYongsheng Zhang0Yongsheng Zhang1Yongsheng Zhang2Li Zhang3Haoyue Lv4Haoyue Lv5Haoyue Lv6Guang Zhang7Guang Zhang8Guang Zhang9Health Management Center, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaInstitute of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaShandong Engineering Laboratory of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaDepartment of Pharmacology, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, ChinaHealth Management Center, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaInstitute of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaShandong Engineering Laboratory of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaHealth Management Center, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaInstitute of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaShandong Engineering Laboratory of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaObjectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors.Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors.Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model’s superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model’s applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846.Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas.https://www.frontiersin.org/articles/10.3389/fphys.2024.1357404/fullhyperuricemiaprediction modelmachine learningstacking ensemblerisk factors |
| spellingShingle | Yongsheng Zhang Yongsheng Zhang Yongsheng Zhang Li Zhang Haoyue Lv Haoyue Lv Haoyue Lv Guang Zhang Guang Zhang Guang Zhang Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population Frontiers in Physiology hyperuricemia prediction model machine learning stacking ensemble risk factors |
| title | Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population |
| title_full | Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population |
| title_fullStr | Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population |
| title_full_unstemmed | Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population |
| title_short | Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population |
| title_sort | ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population |
| topic | hyperuricemia prediction model machine learning stacking ensemble risk factors |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2024.1357404/full |
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