Predictive Modeling in Opioid Misuse Detection: Identifying Risk Factors for Effective Intervention

In recent years, concern about substance use has increased, with the opioid crisis remaining a major public health issue due to high rates of misuse, addiction, and deaths. In 2021, it was estimated that 2.5 million people aged 18 years or older in the U.S. had opioid use disorder in the past year, but only about 1 in 5 received medications to treat it. The total economic burden of opioid use disorder was estimated to be around $471 billion in 2017. This study investigates the use of predictive models for detecting opioid misuse to support decision-makers in the early identification of individuals who may be prone to opioid misuse. This research compares multiple machine learning algorithms, including XGBoost, Random Forest, and CatBoost, to assess their accuracy. After identifying the best-performing model, a feature importance analysis was conducted to determine the most influential variables among demographics, education, employment, and socioeconomic factors. The best model found was the Random Forest classifier, achieving an accuracy of over 87%. These results provide a foundation for healthcare systems to better identify individuals at potential risk of substance misuse, supporting more effective strategies to combat the opioid epidemic.