Predicting depression in old age: Combining life course data with machine learning

[en] With ageing populations, understanding life course factors that raise the risk of depression in old age may help anticipate needs and reduce healthcare costs in the long run. We estimate the risk of depression in old age by combining adult life course trajectories and childhood conditions in supervised machine learning algorithms. Using data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we implement and compare the performance of six alternative machine learning algorithms. We analyse the performance of the algorithms using different life-course data configurations. While we obtain similar predictive abilities between algorithms, we achieve the highest predictive performance when employing semi-structured representations of life courses using sequence data. We use the Shapley Additive Explanations method to extract the most decisive predictive patterns. Age, health, childhood conditions, and low education predict most depression risk later in life, but we identify new predictive patterns in indicators of life course instability and low utilization of dental care services.

Disciplines :

Public health, health care sciences & services
Special economic topics (health, labor, transportation...)
Quantitative methods in economics & management
Sociology & social sciences

Author, co-author :

Montorsi, Carlotta ; LISER - Luxembourg Institute of Socio-Economic Research [LU] ; Unilu - University of Luxembourg [LU] > Department of Social Sciences

FUSCO, Alessio ; University of Luxembourg ; LISER - Luxembourg Institute of Socio-Economic Research [LU]

VAN KERM, Philippe ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) > Socio-Economic Inequality ; LISER - Luxembourg Institute of Socio-Economic Research [LU]

BORDAS, Stéphane ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

External co-authors :

yes

Language :

English

Title :

Predicting depression in old age: Combining life course data with machine learning

Publication date :

November 2023

Journal title :

Economics and Human Biology

ISSN :

1570-677X

eISSN :

1873-6130

Publisher :

Elsevier BV

Volume :

Pages :

101331

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Computational Sciences
Migration and Inclusive Societies

Development Goals :

3. Good health and well-being

Additional URL :

https://api.elsevier.com/content/article/PII:S1570677X23001120?httpAccept=text/xml

FnR Project :

FNR12252781 - Data-driven Computational Modelling And Applications, 2017 (01/09/2018-28/02/2025) - Andreas Zilian

Available on ORBilu :

since 30 November 2023

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Number of downloads

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