Predictors of depression in middle adulthood: A longitudinal machine learning model.
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| Title: | Predictors of depression in middle adulthood: A longitudinal machine learning model. |
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| Authors: | Li, Xiaowen (AUTHOR), Ding, Ling (AUTHOR), Xu, Hao (AUTHOR) |
| Source: | Social Behavior & Personality: an international journal. Jul2025, Vol. 53 Issue 7, p1-13. 13p. |
| Subjects: | Machine learning, Long short-term memory, Panel analysis, Gender differences (Psychology), Satisfaction |
| Abstract: | This study aimed to predict the risk of depression, influencing factors, and gender differences in middle adulthood through applying machine learning models. We selected 2,674 middle-adult-aged participants from the China Family Panel Studies and used a combination of long short-term memory and machine learning models for prediction. Combining long short-term memory modeling with machine learning models significantly enhanced depression prediction among individuals in middle adulthood. Among the six models we examined, the eXtreme Gradient Boosting model performed the best. Further analysis of influencing factors revealed that happiness, self-rated health, and awareness of social issues were the most impactful factors in predicting the risk of depression. Further, the influencing factors varied between genders: for men, happiness, frequency of physical exercise, and job-income satisfaction were paramount, while for women, happiness, job-promotion satisfaction, and self-rated health were the key factors. Implications of the findings are discussed for theory and practice. [ABSTRACT FROM AUTHOR] |
| Copyright of Social Behavior & Personality: an international journal is the property of Scientific Journal Publishers Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 186471511 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predictors of depression in middle adulthood: A longitudinal machine learning model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Xiaowen%22">Li, Xiaowen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+Ling%22">Ding, Ling</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Hao%22">Xu, Hao</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Social+Behavior+%26+Personality%3A+an+international+journal%22">Social Behavior & Personality: an international journal</searchLink>. Jul2025, Vol. 53 Issue 7, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Panel+analysis%22">Panel analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+differences+%28Psychology%29%22">Gender differences (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Satisfaction%22">Satisfaction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study aimed to predict the risk of depression, influencing factors, and gender differences in middle adulthood through applying machine learning models. We selected 2,674 middle-adult-aged participants from the China Family Panel Studies and used a combination of long short-term memory and machine learning models for prediction. Combining long short-term memory modeling with machine learning models significantly enhanced depression prediction among individuals in middle adulthood. Among the six models we examined, the eXtreme Gradient Boosting model performed the best. Further analysis of influencing factors revealed that happiness, self-rated health, and awareness of social issues were the most impactful factors in predicting the risk of depression. Further, the influencing factors varied between genders: for men, happiness, frequency of physical exercise, and job-income satisfaction were paramount, while for women, happiness, job-promotion satisfaction, and self-rated health were the key factors. Implications of the findings are discussed for theory and practice. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Social Behavior & Personality: an international journal is the property of Scientific Journal Publishers Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=186471511 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2224/sbp.14447 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Panel analysis Type: general – SubjectFull: Gender differences (Psychology) Type: general – SubjectFull: Satisfaction Type: general Titles: – TitleFull: Predictors of depression in middle adulthood: A longitudinal machine learning model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Xiaowen – PersonEntity: Name: NameFull: Ding, Ling – PersonEntity: Name: NameFull: Xu, Hao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 03012212 Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: Social Behavior & Personality: an international journal Type: main |
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