Yang, H. orcid.org/0000-0002-3372-4801 and Bath, P.A. orcid.org/0000-0002-6310-7396 (2019) Automatic prediction of depression in older age. In: Proceedings of the 3rd International Conference on Medical and Health Informatics 2019. 3rd International Conference on Medical and Health Informatics (ICMHI 2019), 17-19 May 2019, Xiamen, China. ACM Digital Library ISBN 9781450371995
Abstract
Maintaining good mental health such as the prevention of severe depressive symptoms is critical for physical health and well-being in older adulthood. However, depression in elderlies is not known quite well and thus cannot be treated adequately. In this study, a large and wide variety of influencing factors from multiple domain areas were investigated using a large nationally representative sample of older people from the English Longitudinal Study of Ageing (ELSA). Five different machine learning algorithms were employed to build the models for the prediction of depression in older age. Several model ensemble strategies were proposed to merge the results from individual predictive models in order to further improve prediction performance. Significant risk or protective factors associated with depressive symptoms in the elder were separately identified in each domain area. The findings from this study will enhance our understanding about the underlying pathophysiology of depression, thus helping develop appropriate intervention strategies to prevent or reduce the onset of depression in older age.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Association for Computing Machinery. |
Keywords: | Depression; older people; predictive model; machine learning algorithms; ELSA data |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Aug 2019 12:51 |
Last Modified: | 15 Aug 2019 12:51 |
Status: | Published |
Publisher: | ACM Digital Library |
Refereed: | Yes |
Identification Number: | 10.1145/3340037.3340042 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149766 |