Utilising AI technique to identify depression risk among doctoral students
Abstract The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings are not universally applicable. Using ne...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83617-8 |
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| author | Changhong Teng Chunmei Yang Qiushi Liu |
| author_facet | Changhong Teng Chunmei Yang Qiushi Liu |
| author_sort | Changhong Teng |
| collection | DOAJ |
| description | Abstract The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings are not universally applicable. Using neural network feature extraction technology, this study aims to investigate the factors contributing to the high depression risk of doctoral students and effectively identify doctoral students at depression risk, so as to propose corresponding improvement strategies to prevent and intervene doctoral students with depression risk for universities. Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. Subsequently, we trained the optimal prediction model to predict the doctoral students with depression risk using a Multilayer Perceptron (MLP), achieving an accuracy of 89.09% on the test set. Additionally, this study constructed a group portrait of doctoral students at risk of depression, and found that overwork, poor work-life balance, and poor supervisor-student relationship, etc., were typical characteristics among these students. Finally, we proposed several improvement strategies for higher education institutions. Our research offers a new perspective on utilising artificial intelligence (AI) methods to tackle educational challenges, particularly in the identification and support of doctoral students at risk of depression, thereby enhancing their mental health. |
| format | Article |
| id | doaj-art-3b11f3921496447ea16de3fa7aadb9be |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3b11f3921496447ea16de3fa7aadb9be2025-01-05T12:28:32ZengNature PortfolioScientific Reports2045-23222024-12-0114111510.1038/s41598-024-83617-8Utilising AI technique to identify depression risk among doctoral studentsChanghong Teng0Chunmei Yang1Qiushi Liu2School of Education, Beijing Institute of TechnologySchool of Education, Beijing Institute of TechnologyMediaTek IncAbstract The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings are not universally applicable. Using neural network feature extraction technology, this study aims to investigate the factors contributing to the high depression risk of doctoral students and effectively identify doctoral students at depression risk, so as to propose corresponding improvement strategies to prevent and intervene doctoral students with depression risk for universities. Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. Subsequently, we trained the optimal prediction model to predict the doctoral students with depression risk using a Multilayer Perceptron (MLP), achieving an accuracy of 89.09% on the test set. Additionally, this study constructed a group portrait of doctoral students at risk of depression, and found that overwork, poor work-life balance, and poor supervisor-student relationship, etc., were typical characteristics among these students. Finally, we proposed several improvement strategies for higher education institutions. Our research offers a new perspective on utilising artificial intelligence (AI) methods to tackle educational challenges, particularly in the identification and support of doctoral students at risk of depression, thereby enhancing their mental health.https://doi.org/10.1038/s41598-024-83617-8Depression riskMachine learningDoctoral studentsGroup portrait |
| spellingShingle | Changhong Teng Chunmei Yang Qiushi Liu Utilising AI technique to identify depression risk among doctoral students Scientific Reports Depression risk Machine learning Doctoral students Group portrait |
| title | Utilising AI technique to identify depression risk among doctoral students |
| title_full | Utilising AI technique to identify depression risk among doctoral students |
| title_fullStr | Utilising AI technique to identify depression risk among doctoral students |
| title_full_unstemmed | Utilising AI technique to identify depression risk among doctoral students |
| title_short | Utilising AI technique to identify depression risk among doctoral students |
| title_sort | utilising ai technique to identify depression risk among doctoral students |
| topic | Depression risk Machine learning Doctoral students Group portrait |
| url | https://doi.org/10.1038/s41598-024-83617-8 |
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