Development and Evaluation of a Decision Tree Model for Predicting Drug Addiction Using Demographic, Psychosocial, and Behavioral Factors

This study aims to design a machine learning model to predict drug addiction. The dataset includes various demographic, psychosocial, and behavioral factors contributing to drug addiction. The objective is to create a reliable and accurate predictive model to identify individuals at risk of developing drug addiction. A range of machine learning algorithms, including logistic regression, decision tree, and random forest, were utilized to predict the likelihood of drug addiction. The results indicated that the decision tree model had the highest accuracy in predicting drug addiction. These findings have significant implications for developing effective prevention and intervention strategies to address drug addiction.