Discovering predictors of mental health service utilization with k-support regularized logistic regression

Abstract

Many epidemiological studies are undertaken with a use of large epidemiological databases, which involves the simultaneous evaluation of a large number of variables. Epidemiologists face a number of problems when dealing with large data sets: multicolinearity (when variables are correlated to each other), confounding factors (when risk factor is correlated with both exposure and outcome variable), and interactions (when the direction or magnitude of an association between two variables differs due to the effect of a third variable). Correct variable selection helps to address these issues and helps to obtain unbiased results. Selection of relevant variables is a complicated and a time consuming task. Flawed variable selection methods still prevail in the scientific literature; there is a need to demonstrate the usability of new algorithms using real data. In this paper we propose to use a novel machine learning method, k-support regularized logistic regression, for discovering predictors of mental health service utilization in the National Epidemiologic Survey for Alcohol and Related Conditions (NESARC). We show that k-support regularized logistic regression yields better prediction accuracy than ℓ1 or ℓ2 regularized logistic regression as well as several baseline methods on this task, and we qualitatively evaluate the top weighted variates. The selected variables are supported by related epidemiological research, and give important cues for public policy.