Abstract:
Drug addiction has become a critical health concern, especially among the youth of Bangladesh. According to AsiaNews, there are more than 8 million drug addicts, out of which a large portion consists of young people. Due to this growing menace, some effective preventive steps need to be taken. This research examines some important factors that differentiate between addicted and non-addicted people to help in targeted interventions. This study is based on 1,624 individual responses, collected through an online survey containing a wide array of demographic and behavioral attributes. The dataset includes participants from Dhaka and Sylhet, aged 15 to 27 years, predominantly students. In this paper, a machine learning-based approach was followed for analyzing and predicting the risk of addiction. Models used in this work are Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), CatBoost, and standard classifiers. Ensemble techniques were also adopted, namely Blending (Neural Networks, Gradient Boosting, and K-Nearest Neighbors) and Voting (Logistic Regression, Random Forest, Support Vector Machine). Models were evaluated as well, and their performances corresponded to 98.15% for Blending and CatBoost, 93.85% for Voting, and 99.38% for both MLP and ELM. Top- working models will help identify people in vulnerable positions toward addiction, evaluate the state of health and psychological condition of a person, which will be an indicator for acting in prevention. Outcome variables such as these from the current study are critical in deducing further risks for addiction, especially in targeted public health policies and intervention programs. With a special emphasis on current states of mental and physical health, the high performing models, especially MLP and ELM, proved as reliable tools for assessing risk for addiction. It outlines the role of machine learning in the transformation that views and methods toward the drug addiction crisis among Bangladeshi youth have been causing, with a data-driven basis for prevention and rehabilitation efforts.
