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

Chodhari, Bharat Kumar and Fareed, Mohd and Sharma, Vinod and Mohapatra, Sujit Kumar and Jaswal, Shweta and Devi, Narthana (2024) Development and Evaluation of a Decision Tree Model for Predicting Drug Addiction Using Demographic, Psychosocial, and Behavioral Factors. In: 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), Greater Noida, India.

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Official URL: https://doi.org/10.1109/ICAC2N63387.2024.10895311

Abstract

This study aims to design a machine learning model
to predict drug addiction. The dataset includes various demo-graphic, 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.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Artificial Intelligence and Data Science > AI in Healthcare, Agriculture, Finance
Divisions:	Engineering > Electronics and Communication Engineering
Depositing User:	Unnamed user with email techsupport@mosys.org
Date Deposited:	01 Aug 2026 12:18
Last Modified:	01 Aug 2026 12:18
URI:	https://ir.dsce.ac.in/id/eprint/188

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