A Proactive Approach to Combating the Opioid Crisis Using Machine Learning Techniques

Abstract

The use of big data analytics tools and Machine Learning techniques in identifying and predicting opioid use disorder is a relatively new and emerging area. Previous studies analyzing trends in the opioid crisis have identified an increasingly expensive and worsening epidemic. Many factors contribute to opioid use, abuse, and addiction. Opioid addiction is a complex disease involving various physiological pathways as well as environmental factors. There is some evidence to suggest that people with low education levels and high unemployment and poverty levels are at higher risk of opioid abuse. In this paper, we evaluated different conventional Machine Learning models including Support Vector Machines (SVM), Decision Tree, and Logistic Regression and advanced algorithms like Gradient Boosting. The models were developed to predict opioid abuse disorder using county-level education, poverty, and unemployment data. In contrast, the results suggest that populations with higher socioeconomic status are at greater risk of opioid abuse disorder compared to individuals with lowers. This can be attributed to underlying factors not previously captured increased availability of opioids and resources to acquire them. Identifying which areas and populations are at higher risk of opioid abuse disorder and underlying contributing factors will help inform judicious effective policies, programs, and solutions to tackle the worsening health crisis.