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

Year: 2023

Type: article

Abstract: 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.

Source: Economics & Human Biology

DOI: 10.1016/j.ehb.2023.101331

Authors: Carlotta Montorsi, Alessio Fusco, Philippe Van Kerm, Stéphane P.A. Bordas

Institutions: University of Insubria, University of Luxembourg, Luxembourg Institute of Socio-Economic Research

Language: English

Topic: Health disparities and outcomes

Subfield: Health

Field: Social Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,750

Funders: European Commission, Fonds National de la Recherche Luxembourg, Bundesministerium für Bildung und Forschung, Max-Planck-Gesellschaft, Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche, Seventh Framework Programme, Sixth Framework Programme, Fifth Framework Programme, Horizon 2020, National Institute on Aging

OpenAlex ID: W4388968051