Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors
BackgroundAdolescents often experience difficulties with sleep quality. The existing literature on predicting severe sleep disturbance is limited, primarily due to the absence of reliable tools.MethodsThis study analyzed 1966 university students. All participants were classified into a training set...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1447281/full |
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| author | Lirong Zhang Shaocong Zhao Wei Yang Zhongbing Yang Zhi’an Wu Hua Zheng Mingxing Lei Mingxing Lei Mingxing Lei |
| author_facet | Lirong Zhang Shaocong Zhao Wei Yang Zhongbing Yang Zhi’an Wu Hua Zheng Mingxing Lei Mingxing Lei Mingxing Lei |
| author_sort | Lirong Zhang |
| collection | DOAJ |
| description | BackgroundAdolescents often experience difficulties with sleep quality. The existing literature on predicting severe sleep disturbance is limited, primarily due to the absence of reliable tools.MethodsThis study analyzed 1966 university students. All participants were classified into a training set and a validation set at the ratio of 8:2 at random. Participants in the training set were utilized to establish models, and the logistic regression (LR) and five machine learning algorithms, including the eXtreme Gradient Boosting Machine (XGBM), Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), CatBoosting Machine (CatBM), were utilized to develop models. Whereas, those in the validation set were used to validate the developed models.ResultsThe incidence of severe sleep disturbance was 5.28% (104/1969). Among all developed models, the XGBM model performed best in AUC (0.872 [95%CI: 0.848-0.896]), followed by the CatBM model (0.853 [95% CI: 0.821-0.878]) and DT model (0.843 [95% CI: 0.801-0.870]), whereas the AUC of the logistic regression model was only 0.822 (95% CI: 0.777-0.856). Additionally, the XGBM model had the best accuracy (0.792), precision (0.780), F1 score (0.796), Brier score (0.143), and log loss (0.444).ConclusionsThe XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents. |
| format | Article |
| id | doaj-art-7be7c8aff6354146ab3d6a46f0adb8ca |
| institution | OA Journals |
| issn | 1664-0640 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj-art-7be7c8aff6354146ab3d6a46f0adb8ca2025-08-20T02:12:42ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402024-11-011510.3389/fpsyt.2024.14472811447281Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factorsLirong Zhang0Shaocong Zhao1Wei Yang2Zhongbing Yang3Zhi’an Wu4Hua Zheng5Mingxing Lei6Mingxing Lei7Mingxing Lei8Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, ChinaDepartment of Physical Education, Xiamen University of Technology, Xiamen, Fujian, ChinaDepartment of Physical Education, Xiamen University of Technology, Xiamen, Fujian, ChinaSchool of Physical Education, Guizhou Normal University, Guiyang, Guizhou, ChinaDepartment of Physical Education, Guangzhou Institute of Physical Education, Guangzhou, ChinaCollege of Physical Education and Health Sciences, Chongqing Normal University, Chongqing, ChinaDepartment of Orthopaedics, Hainan Hospital of Chinse PLA General Hospital, Sanya, ChinaNursing Department, The First Medical Center of Chinese PLA General Hospital, Beijing, ChinaChinese PLA Medical School, Beijing, ChinaBackgroundAdolescents often experience difficulties with sleep quality. The existing literature on predicting severe sleep disturbance is limited, primarily due to the absence of reliable tools.MethodsThis study analyzed 1966 university students. All participants were classified into a training set and a validation set at the ratio of 8:2 at random. Participants in the training set were utilized to establish models, and the logistic regression (LR) and five machine learning algorithms, including the eXtreme Gradient Boosting Machine (XGBM), Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), CatBoosting Machine (CatBM), were utilized to develop models. Whereas, those in the validation set were used to validate the developed models.ResultsThe incidence of severe sleep disturbance was 5.28% (104/1969). Among all developed models, the XGBM model performed best in AUC (0.872 [95%CI: 0.848-0.896]), followed by the CatBM model (0.853 [95% CI: 0.821-0.878]) and DT model (0.843 [95% CI: 0.801-0.870]), whereas the AUC of the logistic regression model was only 0.822 (95% CI: 0.777-0.856). Additionally, the XGBM model had the best accuracy (0.792), precision (0.780), F1 score (0.796), Brier score (0.143), and log loss (0.444).ConclusionsThe XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1447281/fullsleep disturbanceadolescentsmachine learningepidemiologyprediction modelPittsburgh sleep quality index |
| spellingShingle | Lirong Zhang Shaocong Zhao Wei Yang Zhongbing Yang Zhi’an Wu Hua Zheng Mingxing Lei Mingxing Lei Mingxing Lei Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors Frontiers in Psychiatry sleep disturbance adolescents machine learning epidemiology prediction model Pittsburgh sleep quality index |
| title | Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors |
| title_full | Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors |
| title_fullStr | Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors |
| title_full_unstemmed | Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors |
| title_short | Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors |
| title_sort | utilizing machine learning techniques to identify severe sleep disturbances in chinese adolescents an analysis of lifestyle physical activity and psychological factors |
| topic | sleep disturbance adolescents machine learning epidemiology prediction model Pittsburgh sleep quality index |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1447281/full |
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