Machine learning-driven identification of key risk factors for predicting depression among nurses
Abstract Background Since the outbreak of the coronavirus disease (COVID-19) in 2019, caused by SARS-CoV-2, the disease has become a global health threat due to its high infectivity, morbidity, and mortality rates. With China’s comprehensive relaxation of pandemic control policies in 2022, the risk...
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BMC
2025-04-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-025-02957-6 |
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| author | Xiaoyan Qi Xin Huang |
| author_facet | Xiaoyan Qi Xin Huang |
| author_sort | Xiaoyan Qi |
| collection | DOAJ |
| description | Abstract Background Since the outbreak of the coronavirus disease (COVID-19) in 2019, caused by SARS-CoV-2, the disease has become a global health threat due to its high infectivity, morbidity, and mortality rates. With China’s comprehensive relaxation of pandemic control policies in 2022, the risk of infection for nursing personnel has further increased. Objectives This study aims to identify risk factors associated with depression among nursing staff during the full reopening of COVID-19 in China in 2022 and to construct a predictive model to assess the risk. Methods From December 9, 2022, to April 6, 2023, a cross-sectional study was conducted in three hospitals in Anhui Province, including 293 nursing staff. The research subjects were divided into a depression group and a non-depression group, and SPSS 23.0 software was used to analyze the data of both groups. We developed four predictive machine learning models: logistic regression, support vector machine, extreme gradient boosting machine (XGBoost), and adaptive boosting (AdaBoost). The development and validation of these models utilized open-source Python libraries such as Scikit-learn and XGBoost. The models were trained and validated using a 10-fold cross-validation method, and the final model selection was based on the area under the receiver operating characteristic curve (AUC). Results The AUC values for the logistic regression, SVM, Logistic, XGBoost, and AdaBoost models were 0.86, 0.88, 0.95, and 0.93, respectively, with F1 scores of 0.79, 0.83, 0.90, and 0.89, respectively. The XGBoost model demonstrated the highest predictive accuracy. However, the study’s findings are limited by the small sample size and single location, and further validation is needed to confirm the model’s generalizability. The extreme gradient boosting machine model, tailored for common risk factors among Chinese nursing staff, provides a powerful tool for predicting the risk of depression. Conclusion This model can assist clinical managers in accurately identifying and addressing potential risk factors during and after the full reopening of COVID-19. Since the working environment and stress factors faced by nursing staff may vary across different countries, the research findings from China can promote international exchange and cooperation in the management of mental health among nursing staff, advice future research should focus on larger, multi-center studies to validate the model’s performance and explore additional risk factors. Clinical trial number Not applicable, because of this article belongs to cross-sectional study. |
| format | Article |
| id | doaj-art-1701ec2b249f42e794f0fb741960bae5 |
| institution | OA Journals |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-1701ec2b249f42e794f0fb741960bae52025-08-20T01:53:04ZengBMCBMC Nursing1472-69552025-04-0124111110.1186/s12912-025-02957-6Machine learning-driven identification of key risk factors for predicting depression among nursesXiaoyan Qi0Xin Huang1School of Nursing, Anhui Medical UniversitySchool of Management, Anhui UniversityAbstract Background Since the outbreak of the coronavirus disease (COVID-19) in 2019, caused by SARS-CoV-2, the disease has become a global health threat due to its high infectivity, morbidity, and mortality rates. With China’s comprehensive relaxation of pandemic control policies in 2022, the risk of infection for nursing personnel has further increased. Objectives This study aims to identify risk factors associated with depression among nursing staff during the full reopening of COVID-19 in China in 2022 and to construct a predictive model to assess the risk. Methods From December 9, 2022, to April 6, 2023, a cross-sectional study was conducted in three hospitals in Anhui Province, including 293 nursing staff. The research subjects were divided into a depression group and a non-depression group, and SPSS 23.0 software was used to analyze the data of both groups. We developed four predictive machine learning models: logistic regression, support vector machine, extreme gradient boosting machine (XGBoost), and adaptive boosting (AdaBoost). The development and validation of these models utilized open-source Python libraries such as Scikit-learn and XGBoost. The models were trained and validated using a 10-fold cross-validation method, and the final model selection was based on the area under the receiver operating characteristic curve (AUC). Results The AUC values for the logistic regression, SVM, Logistic, XGBoost, and AdaBoost models were 0.86, 0.88, 0.95, and 0.93, respectively, with F1 scores of 0.79, 0.83, 0.90, and 0.89, respectively. The XGBoost model demonstrated the highest predictive accuracy. However, the study’s findings are limited by the small sample size and single location, and further validation is needed to confirm the model’s generalizability. The extreme gradient boosting machine model, tailored for common risk factors among Chinese nursing staff, provides a powerful tool for predicting the risk of depression. Conclusion This model can assist clinical managers in accurately identifying and addressing potential risk factors during and after the full reopening of COVID-19. Since the working environment and stress factors faced by nursing staff may vary across different countries, the research findings from China can promote international exchange and cooperation in the management of mental health among nursing staff, advice future research should focus on larger, multi-center studies to validate the model’s performance and explore additional risk factors. Clinical trial number Not applicable, because of this article belongs to cross-sectional study.https://doi.org/10.1186/s12912-025-02957-6DepressionCOVID-19Full reopeningMachine learningRisk factorsCross-sectional study |
| spellingShingle | Xiaoyan Qi Xin Huang Machine learning-driven identification of key risk factors for predicting depression among nurses BMC Nursing Depression COVID-19 Full reopening Machine learning Risk factors Cross-sectional study |
| title | Machine learning-driven identification of key risk factors for predicting depression among nurses |
| title_full | Machine learning-driven identification of key risk factors for predicting depression among nurses |
| title_fullStr | Machine learning-driven identification of key risk factors for predicting depression among nurses |
| title_full_unstemmed | Machine learning-driven identification of key risk factors for predicting depression among nurses |
| title_short | Machine learning-driven identification of key risk factors for predicting depression among nurses |
| title_sort | machine learning driven identification of key risk factors for predicting depression among nurses |
| topic | Depression COVID-19 Full reopening Machine learning Risk factors Cross-sectional study |
| url | https://doi.org/10.1186/s12912-025-02957-6 |
| work_keys_str_mv | AT xiaoyanqi machinelearningdrivenidentificationofkeyriskfactorsforpredictingdepressionamongnurses AT xinhuang machinelearningdrivenidentificationofkeyriskfactorsforpredictingdepressionamongnurses |