Use of Machine Learning to Predict Drug Court Success

Substance use remains a major public health issue in the United States that significantly impacts individuals, families, and society. Many individuals who suffer from substance use disorder (SUD) face incarceration due to drug-related offenses. Drug courts have emerged as an alternative to imprisonment and offer the opportunity for individuals to participate in a drug rehabilitation program instead. Drug courts mainly focus on those with non-violent drug-related offenses. One of the challenges of decision making in drug courts is assessing the likelihood of participants graduating from the drug court and avoiding recidivism after graduation. This study investigates the use of machine learning models to predict success in drug courts using data from a substance use drug court in Missouri. Success is measured in terms of graduation from the program, and the model includes a wide range of potential predictors including demographic characteristics, family and social factors, substance use history, legal involvement, physical and mental health history, employment history as well as drug court participation data. The results will be beneficial to drug court teams and presiding judges in predicting client success, evaluating risk factors during treatment for participants, informing person-centered treatment planning, and the development of after-care plans for high-risk participants to reduce the likelihood of recidivism.