Application of data mining techniques and logistic regression to model drug use transition to injection: a case study in drug use treatment centers in Kermanshah Province, Iran

Autor: Lily Tapak, Khadijeh Najafi-Ghobadi, Somayeh Najafi-Ghobadi, Abbas Aghaei
Rok vydání: 2019
Předmět:
Male
Support vector machine
lcsh:Social pathology. Social and public welfare. Criminology
Logistic regression
030508 substance abuse
Iran
Likelihood ratios in diagnostic testing
lcsh:HV1-9960
Heroin
0302 clinical medicine
Risk Factors
Decision tree
Data Mining
Medicine
030212 general & internal medicine
Substance Abuse
Intravenous
media_common
Aged
80 and over
Drug injection
lcsh:Public aspects of medicine
Health Policy
Middle Aged
Hepatitis B
Psychiatry and Mental health
Female
0305 other medical science
medicine.drug
Adult
Drug
Adolescent
Substance-Related Disorders
media_common.quotation_subject
Young Adult
03 medical and health sciences
Environmental health
Humans
Aged
business.industry
Research
Decision Trees
lcsh:RA1-1270
medicine.disease
Neural network
Logistic Models
Socioeconomic Factors
Neural Networks
Computer
business
Zdroj: Substance Abuse Treatment, Prevention, and Policy, Vol 14, Iss 1, Pp 1-11 (2019)
Substance Abuse Treatment, Prevention, and Policy
ISSN: 1747-597X
Popis: Background Drug injection has been increasing over the past decades all over the world. Hepatitis B and C viruses (HBV and HCV) are two common infections among people who inject drugs (PWID) and more than 60% of new human immunodeficiency virus (HIV) cases are PWID. Thus, investigating risk factors associated with drug use transition to injection is essential and was the aim of this research. Methods We used a database from drug use treatment centers in Kermanshah Province (Iran) in 2013 that included 2098 records of people who use drugs (PWUD). The information of 29 potential risk factors that are commonly used in the literature on drug use was selected. We employed four classification methods (decision tree, neural network, support vector machine, and logistic regression) to determine factors affecting the decision of PWUD to transition to injection. Results The average specificity of all models was over 84%. Support vector machine produced the highest specificity (0.9). Also, this model showed the highest total accuracy (0.91), sensitivity (0.94), positive likelihood ratio [1] and Kappa (0.94) and the smallest negative likelihood ratio (0). Therefore, important factors according to the support vector machine model were used for further interpretation. Conclusions Based on the support vector machine model, the use of heroin, cocaine, and hallucinogens were identified as the three most important factors associated with drug use transition injection. The results further indicated that PWUD with the history of prison or using drug due to curiosity and unemployment are at higher risks. Unemployment and unreliable sources of income were other suggested factors of transition in this research.
Databáze: OpenAIRE
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