Predicting and Understanding Opioid Use Disorder (OUD) Using Ensemble Learning

Overview

Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is quickly becoming a public health epidemic. Many factors increase the odds for developing OUD, but no single factor is a determinant. This suggests that OUD may have a complex etiology, with contributions from various genetic, environmental, and socioeconomic factors and their interactions. This project builds an ensemble learning approach that considers the interplay between demographic, socioeconomic, physical and psychological factors in predicting individuals at risk for OUD. Three tree-based classifiers, namely, decision tree, random forest and gradient boosting are trained on a labeled data set constructed using the responses from the 2017 edition of the National Survey on Drug Use and Health. The gradient boosting classifier can predict adults at risk for OUD with remarkable accuracy, with AUC over 0.90. The random forest classifier reveals that early initiation of marijuana before the age of 18 is the most dominant predictor. Finally, the decision tree classifier reveals that early initiation of marijuana affects some demographic and socioeconomic groups more than others. These groups include young adults between 18-25 years, on probationary status, without any college degree, in fair or poor health, and living in unsafe neighborhoods where drugs are easily available.

Competition history

• AJAS 2020 Category not listed

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