Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits

Abstract

Heroin and cocaine addiction is a global health issue that can result in death. These substances have the ability to change cognitive functions and impulsive behavior control. This study explores the relationship between heroin and cocaine use and the development of additional psychoactive substance addictions, such as cannabis and nicotine. It also investigates whether lighter drug use, like cannabis, leads to heavier drug use. Using subsets of heroin, cocaine, and cannabis data for neural network model training, stacking ensemble learning is employed to uncover these connections. These models predict the risk of subsequent substance abuse based on the history of heroin, cocaine, or cannabis abuse, incorporating demographic factors and personality traits. Results reveal significant impacts: Heroin abuse substantially increases the risk of cannabis and nicotine use (F-scores of 0.95 and 0.94, respectively). Cocaine abuse shows an even stronger association (Accuracy: 0.88) and can lead to heroin and cannabis use. Additionally, cannabis use is linked to subsequent cocaine use. These results have important implications for precision medicine, emphasizing the importance of personalized medications in preventing subsequent addiction development.