A supervised machine learning approach to predict vulnerability to drug addiction
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There are signi cant amount of di erences between an addicted and non-addicted person on their social and familial behavior. In our thesis we tried to nd 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. A primary data set was constructed which include 498 samples. For constructing the questionnaire Addiction Severity Index and WHO's Assist Scale were followed along with the help of psychologists and specialists on drug addiction. For addicted person's data we reached some rehabilitation center of Dhaka and for non-addicted person's data we communicated di erent aged group people of di erent colleges and universities. 498 samples where one sample consisted of 60 features were trained and tested by supervised machine learning approach. Reliability of the data set was validated by Cronbach's Alpha Nominal Test. 10 algorithms were incorporated including Neural Network, Deep Belief Network, Random Forest, XGBooster etc. and their results were compared. Among the algorithms, XGB came up with the highest number of accuracy of 95.20% and KNN delivered the least which is 88.97%. In order to select important features mRMR, Chi-square, Principle Component Analysis techniques were used. From feature selection we got the key features of an addicted person's behavior that were in uential for their drug abuse. This will help people to understand if a person is going to be vulnerable to addiction or not based on their health issues and social and familial behavior.