Predicting Depression History from a Short Reward/Aversion Task with Behavioral Economic Features
摘要
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.