Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques. (arXiv:2101.03184v1 [cs.LG])

Substance abuse is the unrestrained and detrimental use of psychoactive
chemical substances, unauthorized drugs, and alcohol. Continuous use of these
substances can ultimately lead a human to disastrous consequences. As patients
display a high rate of relapse, prevention at an early stage can be an
effective restraint. We therefore propose a binary classifier to identify any
individual’s present vulnerability towards substance abuse by analyzing
subjects’ socio-economic environment. We have collected data by a questionnaire
which is created after carefully assessing the commonly involved factors behind
substance abuse. Pearson’s chi-squared test of independence is used to identify
key feature variables influencing substance abuse. Later we build the
predictive classifiers using machine learning classification algorithms on
those variables. Logistic regression classifier trained with 18 features can
predict individual vulnerability with the best accuracy.



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