GA-SVM for Evaluating Heroin Consumption Risk

TL;DR

This study develops a GA-optimized SVM model to predict Heroin use based on personality, demographics, and drug consumption data, revealing that other drug use is a stronger predictor than psycho-social factors.

Contribution

It introduces a hybrid genetic algorithm-support vector machine approach for improved Heroin consumption prediction, highlighting the significance of other drug use as a predictor.

Findings

Models outperformed previous prediction methods.

Consumption of other drugs is a stronger predictor than psycho-social factors.

Prescription drug use is a significant predictor of Heroin use.

Abstract

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.