Abstract
This paper presents a novel example of depression prediction, merging cognitive science with data-driven machine learning. Behavioral economic features were engineered from a short picture rating task. Relative Preference Theory was applied to rating data for quantifying the degree to which participants liked, disliked, or were neutral to several types of pictures; thus, behavioral economic variables including loss aversion, risk aversion, and 13 others that are amenable to psychological interpretation were mined. These variables were features of a logistic regression predictive model that targeted depression in a population-based sample (N = 281) with high test accuracy and no overfitting. Per our review of the literature, we cannot identify other papers that explicitly use behavioral economic features to predict depression with machine learning.
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This work has been supported by the Office of Naval Research, Grant N000142112216.
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Stefanopoulos, L. et al. (2024). Predicting Depression History from a Short Reward/Aversion Task with Behavioral Economic Features. In: Pino, E., Magjarević, R., de Carvalho, P. (eds) International Conference on Biomedical and Health Informatics 2022. ICBHI 2022. IFMBE Proceedings, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-031-59216-4_5
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DOI: https://doi.org/10.1007/978-3-031-59216-4_5
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