Article
المجلة العلمية :
International Journal of Computers and Applications
ISSN : 1925-7074
الناشر :
Taylor & Francis
معلومات
الفترة : October 2023
المجلد : 45 العدد : 11
الصفحات : 722-733
التفاصيل
Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits
Amina Bouhadja Abdelkrim Bouramoul
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.
الكلمات المفتاحية :
Addiction Machine Learning Substance Use Disorder Ensemble Learning Stacking Ensemble Neural Networks
مرجع الإقتباس :
misc-lab-424
DOI :
10.1080/1206212X.2023.2273011
الرابط :
Texte intégral
ACM :
A. Bouhadja and A. Bouramoul. 2023. Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits. International Journal of Computers and Applications, 45, 11 (October 2023), Taylor & Francis, 722-733. DOI: https://doi.org/10.1080/1206212X.2023.2273011.
APA :
Bouhadja, A. & Bouramoul, A. (2023, October). Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits. International Journal of Computers and Applications, 45(11), Taylor & Francis, 722-733. DOI: https://doi.org/10.1080/1206212X.2023.2273011
IEEE :
A. Bouhadja and A. Bouramoul, "Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits". International Journal of Computers and Applications, vol. 45, no. 11, Taylor & Francis, pp. 722-733, October, 2023. DOI: https://doi.org/10.1080/1206212X.2023.2273011.
BibTeX :
@article{misc-lab-424,
author = {Bouhadja, Amina and Bouramoul, Abdelkrim},
title = {Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits},
journal = {International Journal of Computers and Applications},
volume = {45},
number = {11},
issn = {1925-7074},
pages = {722--733},
publisher = {Taylor & Francis},
year = {2023},
month = {October},
doi = {10.1080/1206212X.2023.2273011},
url = {https://doi.org/10.1080/1206212X.2023.2273011},
keywords = {Addiction, Machine Learning, Substance Use Disorder, Ensemble Learning, Stacking Ensemble, Neural Networks}
}
RIS :
TI  - Beyond cocaine and heroin use: a stacking ensemble-based framework for predicting the likelihood of subsequent substance use disorder using demographics and personality traits
AU - A. Bouhadja
AU - A. Bouramoul
PY - 2023
SN - 1925-7074
JO - International Journal of Computers and Applications
VL - 45
IS - 11
SP - 722
EP - 733
PB - Taylor & Francis
AB - 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.
KW - Addiction
KW - Machine Learning
KW - Substance Use Disorder
KW - Ensemble Learning
KW - Stacking Ensemble
KW - Neural Networks
DO - 10.1080/1206212X.2023.2273011
UR - https://doi.org/10.1080/1206212X.2023.2273011
ID - misc-lab-424
ER -