A machine learning approach to predict vulnerability to drug addiction

A. Shahriar, F. Faisal, S. U. Mahmud, A. Chakrabarti and M. G. Rabiul Alam, "A Machine Learning Approach to Predict Vulnerability to Drug Addiction," 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2019, pp. 1-7, doi: 10.1109/ICCIT48885.2019.9038605.

Abstract

There are significant amount of differences between an addicted and non-addicted person on their social and familial behavior. In this paper we tried to find out the characteristics of a person related to his social and familial life and also health issues that can prove his vulnerability to drug addiction. The research was held on the context of the people of Dhaka, Bangladesh and on an age group of 15 to 40 years. We have collected and analyzed data associated with addicted and non-addicted people from different areas of Dhaka. For addicted person's data we reached some rehabilitation center of Dhaka and for non-addicted person's data we communicated different aged group people of different colleges and universities. A machine learning approach then helped us to find out some features that differentiate between these two groups of people. This will help people to understand if a person is going to be addicted or not based on their health issues and social and familial behavior. Our questionnaire was constructed on the basis of Addiction Severity Index with the help of psychologists and specialists on drug addiction.