Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques
This work addresses the problem of early identification of substance abuse vulnerability for individuals, which could aid in prevention efforts.
This paper proposes a binary classifier to identify an individual's vulnerability to substance abuse by analyzing socio-economic environmental factors. Using logistic regression trained with 18 features, the model achieved the best accuracy in predicting vulnerability.
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.