  There were over 70,000 drug overdose deaths in the USA in 2017. Almost half
of those involved the use of Opioids such as Heroin. This research supports
efforts to combat the Opioid Epidemic by further understanding factors that
lead to Heroin consumption. Previous research has debated the cause of Heroin
addiction, with some explaining the phenomenon as a transition from
prescription Opioids, and others pointing to various psycho-social factors.
This research used self-reported information about personality, demographics
and drug consumption behavior to predict Heroin consumption. By applying a
Support Vector Machine algorithm optimized with a Genetic Algorithm (GA-SVM
Hybrid) to simultaneously identify predictive features and model parameters,
this research produced several models that were more accurate in predicting
Heroin use than those produced in previous studies. Although all factors had
predictive power, these results showed that consumption of other drugs (both
prescription and illicit) were stronger predictors of Heroin use than
psycho-social factors. The use of prescription drugs as a strong predictor of
Heroin use is an important though disturbing discovery but that can help combat
Heroin use.
