Machine Learning techniques applied to the consumption of illegal psychoactive substances: A systematic mapping
DOI:
https://doi.org/10.17981/ingecuc.19.2.2023.08Keywords:
machine Learning, illegal psychoactive substances, illegal drugs, treatment, predictive models, new illegal psychoactive substancesAbstract
Introduction— The consumption of illicit psychoactive substances is a problem experienced every day, by people of different ages who have been involved in it, highlighting that many of these substances generate disorders such as, for example: Marijuana or cannabis: its consumption affects brain function directly, and particularly the parts of the brain responsible for memory, learning, attention, decision making. Bazuco: it is a toxic substance, which main risks of consumption are reflected in the neurological deterioration and in the organism, and its dissolution in the bloodstream is very fast, an aspect that makes it very addictive. Cocaine: its consumption, directly affects the nervous system and the rest of the organism immediately, these affectations include vasoconstriction, mydriasis, hyperthermia, tachycardia and hypertension. Heroin: is a highly addictive substance, initially, its effects are very pleasant, which leads to a continuous and repetitive consumption behavior, in addition, it produces sensations of dry mouth, reddening and heating of the skin, heaviness in arms and legs, nausea and vomiting, intense itching and clouding of the mental faculties.
Objective— This problem is something that stands out a lot and has a great impact on young people according to the context they are in, since nowadays it is very easy to obtain this type of substances, therefore, a series of works have been proposed that address this problem from artificial intelligence.
Methodology— The current study is a review of 50 publications related to the use of ML methods and techniques applied to the consumption of illicit psychoactive substances.
Results— From the publications included, common themes were found, so a summary is made of the articles selected for each theme and the methods adopted are briefly described, as well as a comparison between them, noting the methods used, their results and other important factors of the application or model in different areas, and concluding with a series of proposals on the lines that could guide future research in this field.
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