Yang, H. and Bath, P. (2018) Prediction of loneliness in older people. In: Proceedings of the 2nd International Conference on Medical and Health Informatics. 2018 2nd International Conference on Medical and Health Informatics (ICMHI 2018), 08-10 Jun 2018, Tsukuba, Japan. ACM , pp. 165-172. ISBN 978-1-4503-6389-1
Abstract
Older people are especially vulnerable to loneliness that has become a major public health concern for people in later life. In this paper, we propose a machine learning based approach to predict loneliness probability using two gradient boosting algorithms, XGBoost and LightGBM. The predictive models are built on a large nationally representative sample from the English Longitudinal Study of Ageing (ELSA) with 7 successive waves (2002 ~2015). The system achieves good performance with a high AUC (Area Under Curve) of over 0.88 and a low LogLoss (Logarithmic loss) of 0.22 on the test data by both algorithms. Moreover, we investigate a wide range of variables to identify significant risk factors associated with loneliness. Specific categories associated with important variables are also recognized by the model. Such information will further enrich our understanding and knowledge of loneliness causes in the elder.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 Association for Computing Machinery. |
Keywords: | Loneliness; predictive model; gradient tree boosting; older people; ELSA data |
Dates: |
|
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: | 03 Oct 2018 15:23 |
Last Modified: | 03 Oct 2018 15:23 |
Published Version: | https://doi.org/10.1145/3239438.3239443 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3239438.3239443 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136511 |